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    "# 25 - Synthetic Difference-in-Differences\n",
    " \n",
    "In previous chapters, we looked into both Difference-in-Differences and Synthetic Control methods for identifying the treatment effect with panel data (data where we have multiple units observed across multiple time periods). It turns out we can merge both approaches into a single estimator. This new Synthetic Difference-in-Differences estimation procedure manages to exploit advantages of both methods while also increasing the precision (decreasing the error bars) of the treatment effect estimate.\n",
    " \n",
    "We will discuss Synthetic Difference-in-Differences mostly in the case of **block treatment assignment**. This means we observe multiple units across time and, at the **same** time, some units are treated while other units remain untreated. We can visualize this by a matrix of treatment assignments $D$, where the **columns of the matrix are units** and **rows of the matrix are time periods**.\n",
    " \n",
    "$$\n",
    "D = \\begin{bmatrix}\n",
    "    0 & 0 & 0 & \\dots & 0 & 0 \\\\\n",
    "    0 & 0 & 0 & \\dots & 0 & 0 \\\\\n",
    "    \\vdots \\\\\n",
    "    0 & 0 & 0 & \\dots & 1 & 1 \\\\\n",
    "    0 & 0 & 0 & \\dots & 1 & 1 \\\\\n",
    "\\end{bmatrix}\n",
    "$$\n",
    " \n",
    "To make things more concrete, let's follow along with the example of estimating the impact of Proposition 99 in California's Cigarette consumption. In this case, we only have one treated unit, California, that gets treated (passed Proposition 99) at some point in time (November 1988, to be precise). If we say California is the last column of the matrix, we get something like this:\n",
    " \n",
    "$$\n",
    "D = \\begin{bmatrix}\n",
    "    0 & 0 & 0 & \\dots & 0 & 0 \\\\\n",
    "    0 & 0 & 0 & \\dots & 0 & 0 \\\\\n",
    "    \\vdots \\\\\n",
    "    0 & 0 & 0 & \\dots & 0 & 1 \\\\\n",
    "    0 & 0 & 0 & \\dots & 0 & 1 \\\\\n",
    "\\end{bmatrix}\n",
    "$$\n",
    " \n",
    "Notice that here we are only talking about the case where all treated units get the treatment **at the same point in time**. In the end, we will discuss how to handle **staggered addoption treatment assignment**, where the treatment is gradually rolled out to units, causing them to get it at different points in time. The only thing we'll require in that design is that, once a unit is treated, it doesn't roll back to being untreated.\n",
    " \n",
    "Back to the simple case, where we have all units treated at the same time, we can simplify the treatment assignment matrix to four blocks, each represented by another matrix. In general, as we move down the matrix, we are moving forward in time. We will also group the treated units to the right of the matrix. As a result, the first block in our matrix (top left) correspond to the control units prior to the treatment period; the second one (top right) corresponds to the treated units prior to the treatment period; the third block (bottom left) contains the control units after the treatment period and the fourth block (bottom right) is the treated unit after the treatment period. The treatment indicator is zero everywhere except for the block with the treated units after the treatment period. \n",
    " \n",
    "$$\n",
    "D = \\begin{bmatrix}\n",
    "    \\pmb{0} & \\pmb{0} \\\\\n",
    "    \\pmb{0} & \\pmb{1} \\\\\n",
    "\\end{bmatrix}\n",
    "$$\n",
    " \n",
    "This assignment matrix will lead to the following outcome matrix:\n",
    " \n",
    "$$\n",
    "Y = \\begin{bmatrix}\n",
    "    \\pmb{Y}_{pre, co} & \\pmb{Y}_{pre, tr} \\\\\n",
    "    \\pmb{Y}_{post, co} & \\pmb{Y}_{post, tr} \\\\\n",
    "\\end{bmatrix}\n",
    "$$\n",
    " \n",
    "Again, notice how the post-treatment period is on the bottom and the treated units are to the right.\n",
    " \n",
    "In our example of estimating the effect of Proposition 99, the outcome $Y$ is cigarette sales. We use $pre$ and $post$ to represent the period prior and after the treatment respectively and $co$ and $tr$ to represent the control and treated unit respectively. \n",
    " \n",
    "We will use the above matrix representation whenever we talk about estimating the synthetic control weights, but there is also another data representation which is useful, especially if we are talking about DiD. In this representation, we have a table with 5 columns: one representing the units, one representing the time periods, the outcome column and two boolean columns flagging the treated units and the treatment period. The number of rows in this table is the number of units $N$ times the number of periods $T$. Here you can see what it looks like for the Proposition 99 data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f3a44c1c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:19.034932Z",
     "start_time": "2023-07-27T13:05:16.952320Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from toolz import curry, partial\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "import statsmodels.formula.api as smf\n",
    "import cvxpy as cp\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from matplotlib import style\n",
    "style.use(\"ggplot\")\n",
    "\n",
    "pd.set_option('display.max_columns', 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b1031a79",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:19.053875Z",
     "start_time": "2023-07-27T13:05:19.036571Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>cigsale</th>\n",
       "      <th>treated</th>\n",
       "      <th>after_treatment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1970</td>\n",
       "      <td>89.800003</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1971</td>\n",
       "      <td>95.400002</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1972</td>\n",
       "      <td>101.099998</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1973</td>\n",
       "      <td>102.900002</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1974</td>\n",
       "      <td>108.199997</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state  year     cigsale  treated  after_treatment\n",
       "0     1  1970   89.800003    False            False\n",
       "1     1  1971   95.400002    False            False\n",
       "2     1  1972  101.099998    False            False\n",
       "3     1  1973  102.900002    False            False\n",
       "4     1  1974  108.199997    False            False"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = (pd.read_csv(\"data/smoking.csv\")[[\"state\", \"year\", \"cigsale\", \"california\", \"after_treatment\"]]\n",
    "        .rename(columns={\"california\": \"treated\"})\n",
    "        .replace({\"state\": {3: \"california\"}}))\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "716f81bb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:19.066633Z",
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>cigsale</th>\n",
       "      <th>treated</th>\n",
       "      <th>after_treatment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>california</td>\n",
       "      <td>1986</td>\n",
       "      <td>99.699997</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>california</td>\n",
       "      <td>1987</td>\n",
       "      <td>97.500000</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>california</td>\n",
       "      <td>1988</td>\n",
       "      <td>90.099998</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>california</td>\n",
       "      <td>1989</td>\n",
       "      <td>82.400002</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>california</td>\n",
       "      <td>1990</td>\n",
       "      <td>77.800003</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         state  year    cigsale  treated  after_treatment\n",
       "78  california  1986  99.699997     True            False\n",
       "79  california  1987  97.500000     True            False\n",
       "80  california  1988  90.099998     True            False\n",
       "81  california  1989  82.400002     True             True\n",
       "82  california  1990  77.800003     True             True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.query(\"state=='california'\").query(\"year.between(1986, 1990)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f86b2c0",
   "metadata": {},
   "source": [
    "If we want to go from this table to the matrix representation we discussed earlier, all we have to do is pivot the table by time (year) and unit (state). We'll be going back and forth between these two representations, as one is more convenient for DiD and the other, for Synthetic Controls estimation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b173cfd8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:19.080891Z",
     "start_time": "2023-07-27T13:05:19.068051Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>state</th>\n",
       "      <th>state_1</th>\n",
       "      <th>state_2</th>\n",
       "      <th>state_4</th>\n",
       "      <th>state_38</th>\n",
       "      <th>state_39</th>\n",
       "      <th>california</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>90.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>123.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>95.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>101.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>103.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>108.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>127.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state  state_1  state_2  state_4  state_38  state_39  california\n",
       "year                                                            \n",
       "1970      90.0    100.0    125.0     106.0     132.0       123.0\n",
       "1971      95.0    104.0    126.0     105.0     132.0       121.0\n",
       "1972     101.0    104.0    134.0     109.0     140.0       124.0\n",
       "1973     103.0    108.0    138.0     110.0     141.0       124.0\n",
       "1974     108.0    110.0    133.0     112.0     146.0       127.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_piv = data.pivot(\"year\", \"state\", \"cigsale\")\n",
    "data_piv = data_piv.rename(columns={c: f\"state_{c}\" for c in data_piv.columns if c != \"california\"})\n",
    "\n",
    "data_piv.head()[[\"state_1\", \"state_2\", \"state_4\", \"state_38\", \"state_39\", \"california\"]].round()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0a03a5e",
   "metadata": {},
   "source": [
    "In terms of potential outcomes, we can go back to the outcome matrix to review the causal inference goal here. Since the treatment is only rolled out to the treated unit after the treatment period, we observe the potential outcome $Y_0$ everywhere in the matrix, except for the bottom right block. \n",
    " \n",
    "$$\n",
    "Y = \\begin{bmatrix}\n",
    "    \\pmb{Y}(0)_{pre, co} & \\pmb{Y}(0)_{pre, tr} \\\\\n",
    "    \\pmb{Y}(0)_{post, co} & \\pmb{Y}(1)_{post, tr} \\\\\n",
    "\\end{bmatrix}\n",
    "$$\n",
    " \n",
    "Our goal is to estimate the $ATT =  \\pmb{Y}(1)_{post, tr} -  \\pmb{Y}(0)_{post, tr}$. For that, we need to somehow estimate the missing potential outcome $\\pmb{Y}(0)_{post, tr}$. In words, we need to know what would have happened to the treated unit in the post-treatment period had it not been treated. With that in mind, a good place to start is by reviewing both Diff-in-Diff and Synthetic Control. At first, it looks like they are each doing very different things to estimate that missing potential outcome. Combining them sure feels weird. However, both methods have more in common than we might think. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e74813a",
   "metadata": {},
   "source": [
    "## Diff-in-Diff Revisited\n",
    " \n",
    "In the Diff-in-Diff chapter, we got the treatment effect by estimating the following linear model.\n",
    " \n",
    "$$\n",
    "Y_{it} = \\beta_0 + \\beta_1 Post_t + \\beta_2 Treated_i + \\beta_3 Treated_i  Post_t + e_{it}\n",
    "$$\n",
    " \n",
    "Where `post` is a time dummy indicating that the period is after the treatment and `treated` is a unit dummy marking units as being part of the treated group. If we estimate this model in the California example, we get -27.34 as the estimated $ATT$, indicating a strong negative effect of Proposition 99. This would mean that per capita consumption of cigarettes fell by 27 packs due to Proposition 99. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "81dbc52c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:19.090637Z",
     "start_time": "2023-07-27T13:05:19.082683Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DiD ATT:  -27.349\n"
     ]
    }
   ],
   "source": [
    "did_model = smf.ols(\"cigsale ~ after_treatment*treated\", data=data).fit()\n",
    "att = did_model.params[\"after_treatment[T.True]:treated[T.True]\"]\n",
    "print(\"DiD ATT: \", att.round(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b11c0c92",
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     "start_time": "2023-07-27T13:05:19.091668Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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JviVer5f4+HjWrFnD2WefzYsvvti4Gl17lJWVkZGRAYDWut3P050kQbTCMAxIToPkNIxxx+d9N49VwN58zN35sHsH5p58zFVLoLqawwBOF/Qb0FjTaDzriPWG6TsRvVVhYWG4Qwi7lqb7/slPfsJdd93FrFmzGDVqFEOGDGnT8/7xj3/k9ttvp7Kykuzs7MZpTdpj4cKF/OhHP2Lx4sWcd9557X6e7iTTfXdmAPV1ULifuJKDlH66MZA8/FBy+PhOiSlhLYjbpZjZEjvHZ9fYCgoKSEpKarZCWneS6b67Trin+5YziE5kOJzQLwvPmHGUnzaucbtZVtJY06BhmEoK4qKTZGRk2DZ5iZ5NEkQ3MOLiYeQ4jJHjGreZNTWwf5eVNKQgLoSwIUkQYWJERED2YIzsjhXEjQGDoF+WFMT7sFtvvRW3280999wT7lBELyMJwkbaUxA3QQrifdwzzzwDIAlCdDpJED2AERUNQ0ZiDBnZuK2hIG71bOzA3J2P+elG+GDl8bONxoK4r7G+YSYlheV7EF3n3nvvJTY2NtxhiF5IEkQP1VAQN/plwZnnN25vrSB+wBOFmZndWNOQgnjPN3/+fClSiy4hCaKXaa0gHnWwgIovP8Vc+y6sal4Qb6hpSEFc9DQDBgzgtNNOo66ujiFDhvDAAw8QFdW9H3p++tOfctNNNzFs2DAefPBBbr755sb7Lr/8cl599dUOH2Pr1q3cfvvtVFRUkJWVxZ///Gfi4uKorq7mtttuY9OmTRiGwS9/+UvOPffcDh9P+iC6gJ0/zTXEZhXEDwZqGv7Gq6k4UHB855i4xp6N7iqI94TXzm6WLVuG1+vtUJdvR9ihD2Lo0KFs27YNgB/84AeMGTOm2bxJdXV1OJ3OboutaTwdcWJsl1xyCXfddRfnnHMOzz33HLt27eLWW2/lySefZOPGjdx///0cPHiQ+fPn88Ybb+A4ob9K+iBESKyCeCokp0pBvIe7/vrrgb69HkRTZ511Fp999hmrV69m0aJFpKens3XrVpYuXcodd9zBpk2bcDqd3H333Zx33nk899xzvP7661RXV7Nr1y6uuOIKbrnFmp/t4YcfbpyV9eqrr+bGG2+koqKC7373u+zfv5/6+np++MMfMnv2bK688kruuusuXn/9dSorK5kxYwbDhw/nz3/+c2PCME2TX/3qV6xcuRLDMLj55puZPXt2Y6yJiYl88cUXnH766fzpT3866Xv76quvGj8InH/++cybN49bb72VL7/8ksmTJwPWBxmv18vGjRs7PA28JAjRTMgF8c9OKIgnJDfWNBoK4jJleveYPn162Lqog+nfvz/QPGHNnz+ft956iyeeeIKLLroIgKeffprbbruNefPm8bvf/Q6wusInTJhAenp6u1bJq62tZeXKleTm5gLWfEwrVqwgOzubhx56CIDly5ezfft2rr76at59993G/ZYvX05UVBSXXnopF154IYZhoLXmtddewzRNLrvsMs455xx27txJRkYG//jHPwBrltamfvazn/HEE080Thne1BtvvMHWrVt56623OHz4MJdccknjH/wtW7awYsUKMjIymD17Nh9//PFJw0TDhw9n2bJlXHzxxbz22ms0jJqMHDmSN998k9mzZ7Nv3z42b97Mvn37JEGIrtemgvinn2DW1Vk7NHSIS0G8Sz311FO2Hf7qLg2f2AHOPvtsrr76atauXcu4cePIzs4G4OOPP2482xoyZAhZWVns2LEDsD6NJwWu8Js1axYfffQRhmEwc+bMxiGZWbNmsWbNGnJzc7nnnnv49a9/zfTp0zn77LNPDKdFH330EXPmzMHpdJKamsqkSZPYuHEjsbGxjBs3rmG4h1GjRrF79+6THr9o0SLuuusu7r//fi666CIiIqzh3quuuopt27Yxa9YssrKymDhxYour3LWFJAjRbiF3iEtBvE8JNtT19NNPnzTOP3/+fObPn99sW0ZGRruGyjweT9BP7E3H209Vbz3xd+9UCxwNHjyYJUuWsGLFCn7zm98wZcoUfvzjH4cU56liaHoW2NKU4UOGDOHZZ58FrOGm5cuXA1at4he/+EXjfpdffjk+ny+kmE5FEoToVC13iJ9QEN+1A3Pd6uYd4lk5lA0bSX1KhnSIi0539tln8+9//5vJkyfz1VdfsXfvXgYPHsynn37Ku+++S3FxMR6PhzfffJP77rsPh8PBj3/8Y37wgx9gmiZLly7lwQcfpKCggISEBL7xjW8QExMTdOruiIgIampqGj/hN5g0aRJPP/00c+fO5ciRI6xZs4a77ror5OVHDx48SEpKCvX19TzwwANce+21ABw7dgzTNImOjmbVqlW4XC6GDRvW4desWxKEUupx4DKgSGs9OrDt98DXgGrgK+B6rfWRwH13ADcAdcDNWuuTF5IVPUaLBfHKCtjTvCBe8ebLUF3VpCCe1XwSQymInyTYmL842YIFC7j99tu58MILcTqd3H///bjdbgDOPPNMbr75ZvLz87niiisYO3YsYK0id+mllwJWkXr06NHk5eXxq1/9CsMwiIiI4De/+c1Jx5o3bx7Tp09nzJgx/PnPf27cPmvWLNatW8eMGTMwDIM777yTtLS0kBPEyy+/zJNPPglYVzR985vfBKzEcc011+BwOMjIyODBBx9s9+vUVLdc5qqUugAoB/7eJEFcBKzQWtcqpe4F0FrfppQaCTwLnAVkAm8Dw7TWda0cRi5zDYGdYwNITkzk4KebmxXE2eOHI02mTA9TQdyur124E4QdLnPtiBdeeIFPPvmEX//61+EO5SR9YrpvrfUqpVTOCduWNfnyQ6Bh/b3ZwHNa6yrAr5TajpUsPuiOWEV4GU4nRnsK4pFuq2ejDxbEG1ZNs2PyEj2bXWoQ3waeD9zuj5UwGuwJbDuJUuom4CawlvBLSUnpyhhD5nK5bBPLiewcG5wivpQU8A1utsmsqaZ2t59a/zZq87dTk7+d2nXvYzYpiDszsnDlDMHlG0pEzlBcviE4ktPaVRC382sXztgKCwtDumKmM66q6QpXXXUVV111VbjDaFFnvm5ut7tNvydh/4kppe4EaoFnApuCvXODjoNprRcDixv2scsnKDt/mrNzbNCO+LzJMDYZxlrXkhumidGkIF63O5+67Z9R9cHK449pZ4e4nV+7cMZWWVnZYpdyAzsPMfWl2CorK0/6PWm4tDbo8TvtyO2glFqAVby+UGvdkAT2AAOa7JYF2KO4IGwvpIJ4wxDVSR3iJxTEs3wYcfYviC9YsIDIyEgeeeSRsBzf4XBQW1tr2zMEYamtrT1p6o3WhO0nqpSaCdwGTNFaVzS561Xgn0qpRVhF6qHAR2EIUfQihqe1DvFA0jhFh3jlyLGY8cm26xB/++23w3p8j8dDZWUlVVVVLQ7dud1uqqqqujmy0PSF2EzTxOFw4PF42vS47rqK6VkgF0gBCoG7gTsAN3AosNuHWuvvBfa/E6suUQv8SGu9JITDyFVMIbBzbGCP+BoL4k0SBwV7wKYF8XBP1hcKO/xcW9LXYzvVVUwym2sX6Ou/cB1h1/jMmhoSjpVSvHlDY88Gu3dAxVFrh6Ad4jmQmNItHeJ2fd0a2Dm+vh5b2C9ztbOq2noWry1kgNfNpAGxpMVG4JApH8QJjIgIIvoNx+FNBi4EgnWI57fYId5YEM/yQeYA6RAXPUKfTxDbD1WyYkcJ9SY88UkRLoeBL8HN0BQP141LIyrCgWmaMk+QOIldCuJPP/00sbGxzJkzpzO+LSEa9fkEMSo9msWzB/P6F8Ws3l1GYXkN2w5X4j9SxeAkD+dmx/HUJwfYUliBL9FNTqIHX4IbX5KHpKg+//KJIDqjIN6YONIyrNl0T+G2224DkAQhOp38hQNSYyL41vg0vjU+jX2l1eTll5DnL+VPHxbw8MeFDExwExPp4PMDFby7swyAjNgIHp5tNW69tf0IEU4DX6KH/l77zMsv7KPVKdObJo5PP4G6OitxNCuIB4aqTiiIz5s3r3FOISE6kySIE2R6I7nm9FSuHpPC5weOkZdfyns7SymvrifB42TW0AR8iR6Soo5/qnth6yEKymsAiHAYDErZw6TMaL4+KhmAYzX1REXY57JIYR+nnjI9//iqfi1NmZ7l4955c0kaO57DMhQqOpkkiBYYhsGItGhGpEXznQlprN13lDx/CW99dYTaehgQH8nOkmqm5Hj5y9cGsbe0Gn9xJfnFVew5Ws+x2noAautNrntxGwkeJzmJHnIS3AxK9DAsxUNytBQqxcmaT5l+QkF8jx+zYRLDJgXxgyAFcdHpJEGEIMLp4JwBcZwzII6yqjre21lKnr+Uf2w4wNMbDjA6PZpcn5dzs+PI9cU3uzSttt7kqjEp+Isr8RdXsXZvOfUmzB+bwtzRKZRW1vLPTQcZlGQlj4EJbtwuOdsQzTUriI89q3G7WVlBwYZ1RJccIq5ob6/qEBfhJ30QHbC/rJp38kvJ85ewv6yGSKfB2VmxXD52AINi6nA5Tj7dr6qtZ1dJFQkeF6kxEXx58Bj/b/nuxjMOhwGZcZEsPCuD0enRVNTUUVlrkuhxdsrwgZ2v+QZ7x2fX2E6c7tusr4Oi/c1W9TvVlOltKYi3l11fO5DYpA+ii/SLi+SqMSl8c3QyXx6qZOWOEt7bWcq7Oz8l3u3k/BwvuT4vQ5I8jX/c3S4HQ5OPFxiHpUTxTzWUovIa/MVV+I9YZxpet/VG/XB3OQ98sJ94jxNfQuAqqkQ3Z2XFEh3RNW9m0bOkp6c3m2PHcDghIwsjo/ML4qJvadcZhFJqEFCntd7Z+SG1my06qWvqTLaXO3hl4x4+3ltObb1JljeSXJ+XKTnxpMW2bTx4X2k16/aVk3+kCn9xJbuOVFNTb/LU14eQEOVixY6SxktwfYkechLdxEa2nDjs/GkJ7B1fb4wtWEG8xQ7xLF9jfaOtHeK98bXrDj3iDCIwl9KftNarlVLXA38F6pVSN2utH+u0SHuBCKfB+YOTGRFvUl5Vx+rdZazcUcLTGw/y9MaDjE6LYoovnvOy44g5xR/yBpneSDK9SY1f19ab7CurJiHQg3Gwooa1e8tZvqPk+GPiIvnL13w4DIP84ko8Lod0iIug2lMQB5oUxAPzUUlBvFcKdYjpQmBB4PYtwHTgCPAyIAmiBbFuJxcNSeCiIQkUllfzjr+Ulf5S/rKmgMUfF3JWViy5Pi/jM2OD1iuCcTkMsuOPX/OuRqcwd1QyxZV15BdXsqO4iqPVdY3J4OGPC/n0wDGiXA58iW5O61fCYK/B5IFSpBTBnaogzp6dzVf1W7X05IJ400kMpSDeo4WaICK11tVKqf5Aktb6fQClVHrXhda7pMdGosakMHd0MtsOVZLnL+HdnWW8v6sMr9vJ+QOtK6CGJnvaXIw2DIOkKBdJUbGMz4xtdt93Jqbz1eHKxktwl35exMgUT2OC+MWK3cREOpp1iHdWQVx0j5kzZ+JyuXjttde69DhWh/gIjCEjGrcFK4ibn2+ED5t3iBcPHkZ9ela3FMRF5wk1QWxQSt0BDAReBwgki9KuCqy3MgyDYSlRDEuJ4tsT0vlk31FW+ktYtr2E1788QmacVa/I9XlJj+14V/bgJA+Dk47PAZ+UnMzu/UUA1NWbRDgNvjh4rLFDHOCy4YncODGdetNkVX5pY4d4qGc5ontt3rw5bMcOtSBev3835oaPmhfE+w9svqqfFMRtJ9QEcQNwD1AD/E9g2zkcXyZUtIPLYXBmVixnZsVytLqO1bvKyPOX8M9NB/nnpoOMTI0iN1CviHV3zqcth2E01j6cDoOfTckCoLyqrrEQPjDBGsIqLK/h/tX7G2MdmBBJToKHi4cmMDxF3sh2sWTJEhISEsIdRjMndognp6RwYP/+IB3i77XYId7egrjoPNIH0QU6euVBUXkN7wTmg9pTWm0lkv6xTA3UKyKc7X+ztCW2unqTPU06xP3F1iSGC8/M4JzsOLYWVfDH1fuadYjnJLpJ70BBvK9fUdJedo4NWo4vaEF8jx+K9h/fqYsL4nZ+7XrKVUwG8B3gKiBVa326UuoCIENrrTsrUGFJi41g7ugUrhyVzPbDleT5S3k3v5QPdpcR53YyOdCxPTyl7fWKtnA6DAYGurvxHd9eH/hQ4XY6GJ4S1axDHOB3Fw9keEoU2w4dw19cJR3iokVSELe3UIeYfgnMAP4IPBTYtge4H5AE0UUMw2BochRDk6O4fnwaG/Zb80Et31HCkm1H6BcXQW5OPFN8XvrFdd8ssg1nB0OSPfx0stXF29Ah7i+uahyiWrO7nH9tPRR4jHX5rS/RzQ8m9cPjclBTV4/LYcjwQQfdd999REdHs3DhwnCH0mk6UhDvzg7x3i7UBPEt4Ayt9UGl1N8C2/zAoC6JSpzE5TCY2D+Wif1jqahpqFeU8tzmgzy7+SCnpUSR6/MyeaCXuE6qV7RFQ4d40y7xa8amMH1wPP5AbSO/uIo9pdW4A0Nkf1lTwPp9R8kJNPn5Et2MN6KQz4Bts2jRIoBelSCC6VCHuBTE2yXUBOEEygO3G5J1bJNtohtFRziZPjiB6YMTOHC0hlWB+aAe+riQR9cVMiEzlqm+eCb2jyHCGb5hHYdhkBEXSUZcJOcMiDvp/vGZsTgdBv7iKl7/opiaepOcz0t4YFY2AP/+9JC1wl8IHeJ92S233EJ0dHS4wwib0KdMP7EgngEDfJQPH4WZlCEF8SBCKlIrpR4FqoEfA/uBZKzhpUit9fe7NMLQ9ZoidXuYpom/uIqV/hJW5ZdypLKO2EgH52V7merzclpqFIZh2LYgV1dvsre0Gld0HJmR1QD892s72FVS3bhPWoyLaYPiufr0VAAOHK0hOdrVbR3idn3twN6xgT3is0NBvK16RJEaq3v670AJEIF15rAMuK4T4hOdwDAMBiV5GJTk4VtnpLGx4Cgr/aWs9Jfw5vYjZMRGMMXn5YozYrDjibXTYZCd4CYlxdv4hnjwUl+zDvH84srGhZeq6+q56ZWvcDsdgSEqa5hqTHp0t9ZjRM/RUkE8KSaKQxvXn6Ig7oR+A/pkQbxNl7kGOqezgd1a64Iui6p9+vQZREsqaur4cHc5ef4SNhVUYALDkj3k+uI5f2AcXo+9JvQN9bWrqq3nnfxS8gPrbPiLqzhWW8/141OZMyKZQxU1PL6+CF+CJ7CWuJukKFeHhg/s9HNtatOmTSQkJJCdnR3uUFpk19cOgsd2UkG8YRLDU06ZngNp/Tq1IB7uM4gWE4RSKqTBa611fbsj61ySIFpxqKKGtQfqeH3LfnYeqcJpwIT+1nxQZ/aPJTKM9YoG7X3t6k2TovIaPBEOEjwuth06xu/e3UfR0ZrGfeLdTn46OZPTM2IoqazlSGVdmzrE7fpzPXE9CDuy62sHbYuteUE8H3P3DijYA3V11g6dXBAPd4I41cfHWo4XpIMxAvdL5bCHSI6OYN6Eflw80IO/2OqveCe/lI/2lBMT4eC8wHxQI1KjetzMrw0F8QZDk6N4ZM5gyqvr2FlcxY7iSvKPVJEaY40lf7i7nL9+VNCsQ9yX6GbaoPiQZtm1kzFjxuBy2etMsLdqtSDeMETVQkHcaLj0doCvRxTET/Vb5TvFfaKHsy4r9XDduFQ2FVaQFyhuL9teQlpMhLV+hc9Lltfd+pPZWGykk1Hp0YxKb36Vz4T+Mfz43H6NHeJr91lTpk/1xQPw+hfFbCg42qxDPCnZnrMOLF261Naf0Hu75lOmW5oXxP3Hh6qaTpkeHXt8iMoGBfFgZKqNLmDnN+upYjtWU8+aPWWs9JeyqeAo9SYMTfaQ6/Ny/kAv8d1Qrwjna3ekspaEwPf4ymeHWbb9CPvKqhs7xJNjInlstg/DMNhaVEFEoNPcDh3idv6dA3vH152xBesQZ28+VAeu1juhIB4/ehylcUldWhBvVw3iREqpy4EpQErTJ9Na2+VKJkkQIQg1tsPHalkVmA/KX2zVK8ZnxpDri+fM/rFd9kfRbq9d0w5xM8LDxQOtmXF/ujSfbYcqGzvEcxLdjM2I4aIhCWGJ026v24nsHF+4YzteEM9vvqpfNxXEO2MupruB7wHPAXOBh4FrgOc7HJ2wpaQoF3NGJDNnRDL5xZW8k1/KO/5SPt67j+gIB+dmx5Hr8zIqLbrH1SvaommHeNM/JP8zOZMdxcc7xL88WEldPY0J4pYlfmIjnY0d4jkJbrLi3V0yZfr48eNxOBysXbu2059bdL3mHeKTG7ebZSV4Sw9TsnVDY0G8pQ5xY+Q4jAnndnpsoY4ZfBuYobXeopS6Xmv948AypD/v9IiE7eQkeshJ9DB/bCpbiqx6xXs7y3j7qxJSo11M8cWT6/MyIL5n1yvaIj02kvTY5h3idYGxqLp6E1+ip1mHOMClwxK46cwM6upN3viy2OrfSPB0eCr3wsLCDj1e2JMRF4/bNxhH/+Pl4BYL4rU1YU0QCVrrLYHb1UqpCK31R0qpKaE8WCn1OHAZUKS1Hh3YloR1BpID5ANKa10cuO8OrDUo6oCbtdZvhhin6EJOh8HYjBjGZsTwvTPr+XC3NR/US58e4oWthxic5GGqz8v5Od7Gsfy+xBk4O3A6DP57Uj/geIf4juLKxga+vWXVPLquqPFxqdEufEke5pyWxKj0aOrqTQyDkM/M1q1bR1JSUus7ih6vxYJ4dfUpHtV+oQ4kf6WUGhW4vQVYqJS6FigO8fFPAjNP2HY7sFxrPRRYHvgapdRIrGnFRwUe81elVM+67rAPcLscTPHFc/e0ATx+xRBumJAGmDy6rojrX9rOL1fuZlV+KVW1dmmTCY+GDnFrenbrWvjseDdPfn0Id0/N4rpxqZyWGsW+0moqA6/V5sIKrtHbuH3ZTh76qIBl24/w5cFj1NQFfy0zMjIaxpFFH2QYBoa7a87eQ/2Y93Os+ZcA7sBaSS4WCGkeJq31KqVUzgmbZwO5gdtPAXnAbYHtz2mtqwC/Umo7cBbwQYixim6WGOXi8tOSuPy0JHaVVJG3o4R38ku57/19RLkcnJMdx1Sfl9Hpvbte0RaJUS4Sg6whDpDgcTJtkBd/cRV5/lKWbDsCwP2zchiU5GFrYQWfHTjWrENciK4Q0m+W1vqNJrfXAEM64djpWuv9gefcr5RKC2zvD3zYZL89gW0nUUrdBNwUeA5SUlI6IayOc7lctonlRF0dW0oKjB/cnx+ZJp/sKeHNz4tYuf0QK3aUkBYbyUXD07h4RCqDkmPCEl9HdFdsKSkwcai1FGy9abK/tIrtB8oZl5NEpMvBV9t38Y+NBxr33//yH4n3uPjwP8/gdjkprawhOsKJywad8Q3k59o+4Y7tlAlCKRUDoLU+Gvi6YWW50cAHWuvnuiCmYB8xg16Lq7VeDCxu2Mcul9GF+7K5U+nO2AZGwU1nJLFgTAIf7bHmg/rn+j08vW4PvkQ3U33xXJDjJbHJJ2B57U7mBkYlQGngssfLh0QzLXtoY4f49279D3uBsiPFlAG/f28vH+4ub9YhPjjJw8i08E0JLj/X9unGqTaCau0M4jmsFeP+Efj6D1iLB60AHlRK9dda39fOuAqVUv0CZw/9gIaq3R5gQJP9sgB7NDiIdnG7HJyfYxWvj1TW8m5+KXn+Uh5fX8STnxQxNiOGXJ+XSUHWjBDBNe0QL7n3XmJjjw9VTfXFkxod0axDfFCim/svsa6G+ceGAzgMOmUNcdG7tZYgJgLXAiilIoEbgdla65VKqbOwpgBvb4J4FVgA/Dbw/ytNtv9TKbUIyASGAh+18xjCZhI8Lr52WhJfOy2JPSVVrPSX8o6/hPtX78fjKiR36BHO6edmTHp041VB4tTmz5/f7JNmw8qDDYqP1VJaVdf49ebCCrYdOtbYIe5xOZg5NIHrx1ujvDsOV9LfG2mLDnERXq0liGit9ZHA7YlArdZ6JUDgMtd+oRwk0DORC6QopfYAd2MlBq2UugHYhdWAh9Z6q1JKA59iTRj4X1rruqBPLHq0rHg3145LZd7YFD4rOsZKfwnvfXWIpZ/VkRTl4oIca7GjnERPuEPt0ayC+PG3+u8uHtisQ9xfbCUEsKZbuWVJPkaTDnFfooeJmTHyc+iDWksQ+5RSp2utNwEXAe823KGUSgCqQjmI1vrqFu66sIX9fw38OpTnFj2fwzAah0vumJnE0o35rPSX8p/PD/PyZ4fJSXCT6/NyQY6X5Gj7TGRmF8uWLcPr9TJp0qSQHxNsDXEApwNuu6B/sw7x93aWEeVykJPoobC8mr+sKeiWDnERfq0liD8Ay5RSq4GLga83ue9iYFNXBSb6JrfLwXkDvZw30EtJZS3v7Swjz1/Ck58c4O8bDnB6ejS5vngmDYhrXF2ur7v++uuBzlkPItLp4JwBcc06xMur6xqvHCmrqqe8ur5Zh7jLYXBXbhbj+sVwsKKG/WXVndIhLsLvlAlCa/1YoA9hIrBIa/1ek7uPAb/oyuBE3xbvcXHp8EQuHZ7I3tJq8vxWf8UfP9iP+6MCJg2w5oMamxHTp+sV06dPJzKy65ZZjW2yPsaQZA+LZuU0doj7Ayv6ZcVbx/9wdxmPrLWuN2noEM9JcHP9eQldFp/oOjLddxfo65fNdURr8ZmmyWcHjpHnL+W9XaUcra4n0ePkghwvub54fInuLluExc6vnV1iK62qY/uhY/iLq8gPXIZbUF7Nku+eQ0VpMXrLQdbvs9bZaBimCveU6XZ57YKx84pyQtiOYRiMTItmZFo035mYxtq95eT5S3n9y2Je+byYgfGBeoXPS4rUK7qd1+1kfGbzDvGaunqiI51UAHGRTgxo1iEe53byj28MwTAMPt5TjsOgU9YQFx0nCUL0WJFOB+dmezk320tpVR3v7yxlpb+UpzZY9Yox6dHk+ryckx1HdISMh4dLRJOO7lnDEpk1LLFxDXH/kSqOVtc1JoJnNh3AX2xd+xLvdpKT6GZCZiyzR1iTEdabpvRsdCNJEKJX8LqdjX989pdZ9Yo8fykPfljAQx8XMinLqleM69f76hX9+1sz0XRGkbq7NKwh3nQdcYBfTc9utoa4P/B/gxtf/gqv+/g6G75As19sD1tHvKdoU4JQSg0A+mutP2x1ZyHCpF9cJFefnspVY1L4/OAx3vGX8t7OUlbtLCXB4+T8HC+5OfEMTuq6eoVon5bWEAeorTe5IMfLjuKqxg5xgNmnJfLtCelU19Xzry2HGhOHdIh3XKgrymUDzwLjsOZFilVKXQnM1Fp/p+vCE6L9DMNgRGo0I1KjuWFCOuv2WfNBLfnyCP/5vJgsbyRTffFM8XlJjem59Yq9e/fautDaWVwOgwVnpDV+XXysFn9xZWNvTEFZDS9sPdSsQzwnwc3Vp6cwrl8MNXX11JtIh3gbhHoG8TDwOnA+cCiw7S3aP82GEN0qwmkwaUAckwbEUVZVx/u7rPmg/rHxAE9vPMCo9Gim+rycK/WKHqNhyvQG2QlunlPDGjvE8wOX4DY08W3YX8H/rdrTrEPcl+DmfG9iuL4F2ws1QZwFXKq1rldKmQBa6xKlVHzXhSZE14hzO5k5NJGZQxMpKKvmnfxS8vwl/OnDAh7+uJCzsmLJzYnnjMwY6RDuYVrqEAfoFxfB3NHJzTrEAZ7OSiMOWLe3nE2FFdIh3kSoCaIQaw2ILxs2BFZ+29UVQQnRXTLiIvnmmBTU6GS+PFRJnr+Ed3eW8d7OMuLdTiYH5oMakmTfeYgWLFhAZGQkjzzySLhDsbWseDfXnJ7a+HV5dR07i6sYkBjFkcMV+I9UndQhnh0fyb0XDyTS6eDA0RqiXI4+1SEeaoL4A/CaUuo3gEspdTXwM6wJ94To8QzDYHhKFMNTovj2+HTW77f6K5ZtO8LrXxTT3xvJpaMqmZjmJD2267qW2+Ptt98Odwg9UkNBvOEs4cpRyVwxIqlZh/ihY7VEBi7TfWRtIWv2lDfrEB+WHMWZWSevCthbhNxJrZSag7V620CsM4eHtdYvd1lkbSed1CGwc2xgv/jKq+tYvauMlTtK+PTAMQBGpUWR64vn3Ow4W1xe2Z7J+rqb3X6uTYUa29aiCj4/cKyxQ3xfWTVDkz387uIcAP66pgCHQad2iIe7kzqkBKGUOjuw1OiJ28/SWttlrQZJECGwc2xg7/hqImP59/p88vyl7C2tJsJhcGZWLLk+L+P7xRLhDN94tZ1fN7B3fO2Nraq2ntKqusYr4P53xW6+OHiMipp6ABwGzByawHfPzABgw/6jDIiPbFOHeLgTRKhDTG8B3iDblwJJ7QtLiJ6ln9eDGp3C3FHJbD9cyUp/Ke/ll7J6VxlxbifnD4wj1xfPsGSP9Ff0AW6Xg9QmZwj/O20ApmlSdLSGHYGrqLK8bgDKquq4e8VugECjn3UV1XnZcQxLObmgbhetrUntwMosRmA96qa/9YOxFvQRok8xDKPxSplvj0/jk31Hycsv4e2vSnjjyyNkxkUwxRdPbo73pE7hrvD0008TGxvLnDlzuvxY4tQMwyA9NpL02MhmU6Z7XA7+b3o2/iOVgUWarIJ4Zlwkw1Ki2FNSxX3v7yOnsUPcjS/BQ0oYvxdo/QyiFqsxruF2U/XIoj6ij3MFhpnOzIrlaHUdH+wuY6W/lGc3HeTZTQcZkRpFrs/L5Gxvl139cttttwFIgrCxCKdxUod4Xb1JXWCIv7rOxOtxsX5fOSsCHeIAi+ZEMjgG9pdZhfPu7hBvLUH4sM4a3gEuaLLdBA5orY91VWBC9DQxkU6mD05g+uAEDhyt4R1/KSv9Jfzto0IeWVvEmf1jyPXFMyGzc+sV8+bNw+12d9rzie7hdBg4A4Myg5I8/GLaAOB4h3h+cRXDUmOoqyjlw91lPPnJAeB4h7gv0c28sanEdeFlt6EWqX+qtf5DkO23aK0XdUlkbSdF6hDYOTawd3ztic00TXYUV7HSX8Kq/FJKKuuIi7RWzcv1eTktJapT6hV2ft3A3vH1hNiariHe0CG+u6SKx78+pPEy3PbqjCL1/8PqhTjRzwG7JAghbMcwDAYneRic5OH6M9LYsP8oef5SVuwoYem2I2TERpDrsxY76tcN9QrRMwXrEDdNs8svhmitSD2tYT+l1FSaZ5lBQFlXBSZEb+N0GEzoH8uE/rFU1NTxwa4y8vylPL/5EM9tPsTwlCim+qz1uL1tGDYoKCigurq6S5cdFfbTHVfKtXYG8VjgfzfweJPtJtb0G//dFUEJ0dtFRzi5cHACFw5O4GBFDav81uSBD31cyKPrCpmQafVXnNk/ttmCO8FMmDAB6FnrQYie4ZQJQmvtA1BK/V1rfV33hCRE35ISHcHXRyVzxcgk/MVV5AXqFWv2lBMT6WBytlWvGJEavF6Rnp6OwyFTWIvOF1INQmt9nVIqApgEZGqtn1dKxQTuO9qVAQrRVxiGwaAkD4OSPCw4I42NBVa9Is9fwpvbj5AeG8GUHC9TffFkeo8PJ61fv97WhVbRc4W6YNAY4FWgCsgCngemAAuAb3ZZdEL0UU6HwfjMWMZnxnKspp4Pd5eR5y/hha2H0FsOMSzZQ64vnvMHxuH1yMrBomuEel76N+D/aa1PA2oC294BJndJVEKIRlERDqYOiucXF2bz6JzBfOuMVKrrTBavLeRbL23nV3l7WLHtINV19eEOVfQyoX70GAU8HbjdsGDQUaWUfScREaIXSo6O4IqRyVwxMpn8Yms+qHu//02eqjc586eLOTc7jqm+eEakRcl6zKLDQk0Q+cAEYG3DBqXUWcD2LohJCBGCnEQP1yd6+Pluax2vs7JieXdnKW99VUJajIsLcuKZ6vOSFS9d1qJ9Qk0QdwGvK6UeAiKVUncA3wNu7LLIhBAhWbJkCQkJCWRnZ/K92oZ6RSkvfXqIF7YeYkiSh1yfl/NzvCRIvUK0QVsWDBoPfAdrwaDdwCNa63VdGFtbyVQbIbBzbGDv+HpabIeP1fJuvjUflL+4CocB4/tZ80GdlRXb4cVsOhqfXfT12Do01YZSyom1FvVIrfX3Ozc0IURXSYpyMXtEErNHJLHziNVf8U5+KWvf30eUy8G52XHk+ryMTo+WeoUIqtUEobWuU0rVAR6sy1w7lVLqx1hnJiawGbgeiMa6lDYHq/6htNbFnX1sIXqD++67j+joaBYuXNjiPgMT3Cw4I435Y1PZWlTBSn8p7+8qY/mOElKiXUzJ8ZI7KJ5sqVeIJkIdkPwjoJVS/wfs4fgaEWitd7T34Eqp/sDNWGcnx5RSGrgKGAks11r/Vil1O3A7cFt7jyNEb7ZokTVf5qkSRAOnw+D0jBhOz4jhe2ems2ZPOXn+Ev792WFe/PQwg5Pc5PriuWCgl4QoqVf0daH+Bvw58P+ME7abQEcnI3cBUUqpGqwzh33AHUBu4P6ngDwkQQgR1C233EJ0dHTrO57A7XJwQY6XC3K8HDlWy7s7S1npL+WxdUU8sb6IcRkx5Pq8TBoQ1631CmEfIRepu4pS6odYK9MdA5ZprecppY5orROa7FOstU4M8tibgJsAtNYTqquruynqU3O5XNTW2nM1VjvHBvaOr6/Eln+4gqWfFbHs8wMUllcRFeEkd0gyM09L44yseJyOttcr+spr19m6I7bALMBBf6hhTRBKqUTgRazpOo4A/wJeAP4cSoI4gVzFFAI7xwb2jq+vxVZvmmwtqiDPX8rqXWVU1NSTHOViSmD9ioEJodcr+tpr11lsfxUTgFLKBXwfa/6llKZPprW+oKXHhWA64NdaHwgc5yXgXKBQKdVPa71fKdUPKOrAMYTo1TZt2hTog8ju1Od1GAZj0mMYkx7DTRPT+XivVa945bPDvPTpYXyJbnJ9Xi7IiSdJ6hW9Uqg/1fuBacBirOGgO4GFwHMdPP4uYJJSKhpriOlCrG7to1gTAf428P8rHTyOEL3WrFmzgK5dD8LtcjB5oJfJA72UVFr1ijx/KU+sP8BTnxzg9IwYpgbqFR6pV/Qaof4kvw7M0lo/ANQG/p8DTO3IwbXWa7CGlNZjXeLqwEpCvwVmKKW2YRXGf9uR4wjRm40ZM4Yzzjij244X73Fx2fAk/jAzh79c5uMbI5PZV1rF/av3s+DFbdy/eh8b9h+lrj689U3RcaGeQURjdU8DHFNKRWutP1dKdfi3Umt9N3D3CZursM4mhBCtWLp0adjG0bPi3cwfl8o1Y1P47MAx8vwlvL/TmuojMSrQX+HzkpLS7aGJThBqgvgMOBP4CGsI6H+VUqWArHEohMBhGIxKi2ZUWjQ3NtYrSvnP54d5+bPDDE4uYnJ2DFNyvCRHR4Q7XBGiUBPED4G6wO1bsNaHiCNwiakQQjSIdDo4L9vLedleSitreXdnGe/vqeCpTw7w908OcHpGNLm+eCYNiCU6oqNtVKIrhb0PohPJZa4hsHNsYO/47Brb+PHjcTgcrF27tvWdwyQlJYVNO/aRl19Cnr+UwvIa3E6DswfEMdXnZWxGTLv6KzorNjv+XKHnXOY6rYW7qoA9Wuud7QtNCNFRhYWF4Q4hJJneSK45PZWrx6Tw+YFjgfmgSlmVX0qix8n5gfW2fYluDJk80BZCHWJ6DMgM3D4EJAduFwEZSqlNwFVa622dHJ8QohXr1q0jKSkp3GGEzDAMRqRFMyItmhsnprF271Hy8kt448tiXv28mOz4SGs+qBwvqTFSrwinUC9zfQx4EEjQWmcCCcADwEOB2x8Df+2C+IQQrcjIyGgYJuhxIpwOzsmO444Lsnjy60P53pnpREc4+fuGA9z48lf8/O1dvP3VESpq6lp/MtHpQk0QPwTu0FofAwj8fyfwI631UeAnwMSuCVEI0RfEuZ3MGpbIvRcP5KHLB3HVmBQOHK3hTx8WsODF7fzhvb2s3Vsu/RXdKNQhpqNYl7l+0GTbBKAicLu+M4MSQoTu1ltvxe12c88994Q7lE7TLy6Sq05P4ZtjkvniYCV5/hLe21nKuzvLiPc4OX+g1V8xJMkj9YouFGqC+H/AMqXUq1gNc1nA14D/Dtx/IVZHtBCimz3zzDMAvSpBNDAMg9NSozgtNYobJqSzfl85K/2lLN12hNe+KCbLG0muz8uUnHjSYqVe0dnasib1SOAbWMXq/cALWutPuzC2tpLLXENg59jA3vHZNbann36a2NhY5syZE+5QWtTZr115VR3v7yojz1/CpweOATA6LYpcXzznZscRExl6f4Vdf64Q/stcpQ+iC/T1X7iOsHN8Elv7dWV8heXV5PlLyfOXsK+shkinwZn9Y8n1eRmfGYurlf4KO7924U4QLQ4xKaUWa61vCtz+B02WGW1Ka31dJ8QohBDtkh4byTfHpKBGJ7PtkFWveHdnGe/vKsPrdnL+wDhyffEMTZZ6RVudqgbhb3J7e1cHIoRon2XLluH1epk0aVK4QwkrwzAYlhLFsJQovh2oV+T5S1m2vYTXvzxCZlwkU31epvi8pMdGhjvcHkGGmLpAXz9l7Qg7x2fX2Pr37w907XoQHRXO1668uo4PAvWKLUVWvWJkqlWvOC87jpz+6bb8uYKNh5gAlFLnAZdrrW8Lct9vgZe11h92RpBCiPaZPn16w7rCIojYSCczhiQwY0gCReU1vBOYD+qvHxWweG0hkwcd5txMD+MzY4lwyhBUU61d5vozWu6QfgerWe5rnRqREKJNnnrqKdue3dhNWmwEc0encOWoZLYfriTPX8p7u0rJ236IOLeTydlxTB0UzzCpVwCtJ4hxwNIW7nsLawoOIYToUQzDYGhyFEOTo/ifGSN4e8su8vwlLN9RwpJtR+gXF0FuTjxTfF76xfXds7PWEoQXiMRaL/pEEVhrQgghRI/lcjqY2D+Wif1jqaipY/Uua0W85zYf5NnNBzktJYpcn7Ued5y7b61f0VqC+By4CHglyH0XBe4XQoRRTyhS9xTREU6mD05g+uAEDhyt4Z18q7/ioY8LeXRdERP7x5Dri2diZgwRzlCnsuu5WksQ9wMPK6WcWAXpeqWUA5gD/AVrdTkhhOh1UmMiuHJUMt8YmcSO4iry/CWsyi/lw93lxEY6mDzQS26Ol9NSo3ptveKUCUJr/U+lVAbwFOBWSh0EUoBK4G6t9bPdEKMQ4hT27t0rReouZBgGg5M8DE7y8K0z0thYcJSV/lJW7Chh6bYjZMRGMMXnJTcnnkxv76pXtDpZn9Z6kVLqUeAcrIWCDgEfaK1Luzo4IYSwE6fDYHxmLOMzrXrFh7vLWekvQW8+xPObDzE8xUOuL57JA714e0G9IqTZXAPJ4M0ujkUIIXqM6Agn0wbFM21QPAcraliVX0qev5SHPy7ksXWFTMiMZYrPy5n9Y4nsofWKUKf7FkLY1IIFC4iMjOSRRx4Jdyh9Vkp0BF8fmczXRybjL7b6K97JL2XNnnJiIh2cl23NBzUiNQpHD6pXSIIQood7++23wx2CaMKX6MGX6OG6calsKqwgb0cJ7wTmhEqLiSDX5yXXF0//HlCvkAQhRA/3xBNP4PV6wx2GOIHTYXBGvxjO6BfD92rqWbOnjJX+Ul7Yegi95RBDkz3k+rycP9BLvMeef4rtGZUQImQXXXSRXMVkc1ERDnJ98eT64jlUUcO7O616xSNri3h8XRHjM63+ijP7x+J22adeIQlCCCG6UXJ0BHNGJDNnRDL5TeoVH+/dR3SEg3Oz45jqi2dkWlS4Q5UEIURP1xOWHBXB5SR6+Faih2vHpbKlqII8fwnv7Szj7a9KSI12MXPkUc7OiGBAvDss8cl6EF3Azqf7do4N7B2fXWPrCVNt2PW1A/vFVllbz5rd1nxQGwqOUm/C4CQPU31ezs/xktDJ9Yp2rwfRHZRSCcCjwGisZU2/DXwBPA/kAPmA0loXhydCIext3rx5uN3h+YQpOp/H5WCKL54pvniMKC8vr88nz1/Co+uKeHx9EWf0s+oVZ2d1fb3CDtWQB4ClWuvTgLHAZ8DtwHKt9VBgeeBrIUQQv/vd7/jb3/4W7jBEF0iOiWT2iCTuv8THny71ccWIJHYeqeK+9/ex4MXtPPDBfjYVHKW+i0aCwnoGoZTyAhcA3wLQWlcD1Uqp2UBuYLengDzgpFXthBCir8hOcHPdGWnMH5fKlsIK8vylrN5VxoodJZydFcvPpmR1+jHDWoNQSo0DFgOfYp09rAN+COzVWic02a9Ya50Y5PE3ATcBaK0nVFdXd0PUrXO5XNTW1oY7jKDsHBvYOz67xrZv3z5cLhdpaWnhDqVFdn3toGfHVllTx3s7DhMd6eRcX1K7jhFYrtaWNQgXMB74b631GqXUA7RhOElrvRgrwQCYdik02a3o1ZSdYwN7x2fX2Hw+HyBF6vbq6bGNSzaA+nZ/D4EidVDhrkHsAfZordcEvn4BK2EUKqX6AQT+LwpTfELYXnp6Ov369Qt3GKIXCmuC0FoXALuVUsMDmy7EGm56FVgQ2LaA4CvaCSGA9evXk5+fH+4wRC8U7iEmgP8GnlFKRQI7gOuxEpdWSt0A7ALmhjE+IYTok8KeILTWG4CJQe66sJtDEUII0US4axBCiA6aOXMmkyZNCncYohcK+xmEEKJjNm/eHO4QRC8lCUKIHm7JkiUkJCSEOwzRC0mCEKKHO/300219Lb/ouaQGIYQQIig5gxCih7vvvvuIjo5m4cKF4Q5F9DKSIITo4RYtWgQgCUJ0OkkQQvRwt9xyC9HR0eEOQ/RCkiCE6OF+8pOfSJFadAkpUgshhAhKziCE6OE2bdpEQkIC2dnZ4Q5F9DKSIITo4WbNmgXYez0I0TNJghCihxszZgwul7yVReeT3yoherilS5dKkVp0CSlSCyGECEoShBBCiKAkQQjRw40fP56cnJxwhyF6IalBCNHDFRYWhjsE0UtJghCih1u3bh1JSUnhDkP0QjLEJEQPl5GRQWZmZrjDEL2QJAghhBBBSYIQooe79dZbZapv0SUkQQjRwz3zzDM8/vjj4Q5D9EJSpBaih7v33nuJjY0NdxiiF5IEIUQPN3/+fJlqQ3QJGWISQggRlJxBCNHDLVu2DK/Xy6RJk8IdiuhlJEEI0cNdf/31gKwHITqfJAgherjp06cTGRkZ7jBELyQJQoge7qmnnpIitegSUqQWQggRlC3OIJRSTmAtsFdrfZlSKgl4HsgB8gGltS4OX4RCCNH32OUM4ofAZ02+vh1YrrUeCiwPfC2ECKJ///643e5whyF6obAnCKVUFnAp8GiTzbOBpwK3nwLmdHNYQgjR59lhiOmPwK1AXJNt6Vrr/QBa6/1KqbRgD1RK3QTcFNiPlJSULg41NC6XyzaxnMjOsYG947NrbFVVVbhcLmpra8MdSovs+tqBxHbK44ftyIBS6jKgSGu9TimV29bHa60XA4sDX5p2uYrDzleU2Dk2sHd8Elv72Tm+vh7bqdYSCfcQ03nA5UqpfOA5YJpS6mmgUCnVDyDwf1H4QhRCiL4prAlCa32H1jpLa50DXAWs0FrPB14FFgR2WwC8EqYQhRCizwr3GURLfgvMUEptA2YEvhZCCNGN7FCkBkBrnQfkBW4fAi4MZzxCCNHX2fUMQgghRJhJghBCCBGUJAghhBBBSYIQQggRlGGaZrhj6Cy95hsRQohuZgTb2JvOIAy7/FNKrQt3DD0xNrvHJ7H1zvgkNgxa0JsShBBCiE4kCUIIIURQkiC6xuLWdwkbO8cG9o5PYms/O8cnsbWgNxWphRBCdCI5gxBCCBGUJAghhBBB2WayPrtTSj0ONCxwNDqwbSzwEBAL5APztNalSqkIrCVUx2O9xn/XWv8m8JgJwJNAFPAG8EOtdYfG+TojNqVUNPAvYDBQB/xHa93htcA763Vr8nyvAoManssOsSmlIoE/A7lAPXCn1vpFG8V3NfAzrF6hfcB8rXWHVqFpY2yRwMPARKzX54eByTnt8H4IGptN3g8tvm5Nnq/T3g/ByBlE6J4EZp6w7VHgdq31GODfwP8Ets8F3IHtE4DvKqVyAvf9DWuZ1KGBfyc+Zzhj+4PW+jTgDOA8pdQsG8WGUurrQHknxNTZsd2J9YYfBowE3rFLfEopF/AAMFVrfTqwCfhBN8d2I0Bg+wzgPqVUw9+ecL8fThVbuN8Pp4qtK94PJ5EEESKt9Srg8AmbhwOrArffAr4RuG0CMYE3ZxRQDZQGVsfzaq0/CHxK+jswxw6xaa0rtNYrA89XDawHsuwQG4BSKha4BfhVR2Pq7NiAbwO/CTxnfUc/nXdyfA2NUDFKKQPwYp1FdGdsI4HlgccVAUeAiTZ5PwSNzSbvh6CxQde8H4KRBNExW4DLA7fnAgMCt18AjgL7gV1Yn0QOA/2BPU0evyewzQ6xNVJKJQBfI/DLaZPY7gHuAyq6KKZ2xRZ4rQDuUUqtV0r9SymVbpf4tNY1wEJgM1ZiGAk81s2xbQRmK6VcSikf1hnOAOzxfmgptkZhfD+cKrZueT9IguiYbwP/FWiHj8P61AZwFta4ZSbgA36ilBpE8Jb2rrrOuK2xARD4BPos8KDWeocdYlNKjQOGaK3/3UXxtDs2rPH+LOB9rfV44APgD3aJL1CbWIg1TJKJNcR0RzfH9jjWH/+1wB+B1UAt9ng/tBQbEPb3Q9DYuvP9IEXqDtBafw5cBKCUGgZcGrjrGmBp4NNbkVLqfaxTw3dpfpqaRSec7ndSbA2//IuBbVrrP3ZFXO2MLRmYoJTKx/qdTVNK5Wmtc20Q27+wPsU1vFn/BdzQ2XF1IL7kwOO+CjxGAx0utrYlNq11LfDjhv2UUquBbUAxYX4/nCK2BmF7P5witil00/tBziA6QCmVFvjfAfwc60oEsE7xpymlDKVUDDAJ+FxrvR8oU0pNCowHXwe8YofYAvv+CogHftQVMbU3Nq3137TWmVrrHGAy8GVXvBnaGZsJ/AfrCiawlsr9tCtia098wF5gpFIqNbDfDOCz7oxNKRUdiAml1AygVmv9qR3eDy3FFvg6rO+HU7xu3fZ+kDOIECmlnsX6I5CilNoD3A3EKqX+K7DLS8ATgdt/CdzegnUa/YTWelPgvoUcv6xvSeBf2GNTSmVhXY3zObBeKQXwZ631o+GOrSPH76bYbgP+oZT6I3AAuN5O8SmlfgGsUkrVADuBb3VzbGnAm0qpeqyEdW2Tpwr3+yFobDZ5P5zqdesWMtWGEEKIoGSISQghRFCSIIQQQgQlCUIIIURQkiCEEEIEJQlCCCFEUJIghBBCBCUJQgghRFD/HyMgFAMgbo/OAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pre_year = data.query(\"~after_treatment\")[\"year\"].mean()\n",
    "post_year = data.query(\"after_treatment\")[\"year\"].mean()\n",
    "\n",
    "pre_control_y = did_model.params[\"Intercept\"]\n",
    "post_control_y = did_model.params[\"Intercept\"] + did_model.params[\"after_treatment[T.True]\"]\n",
    "\n",
    "pre_treat_y = did_model.params[\"Intercept\"] + did_model.params[\"treated[T.True]\"]\n",
    "\n",
    "post_treat_y0 = post_control_y + did_model.params[\"treated[T.True]\"]\n",
    "\n",
    "post_treat_y1 = post_treat_y0 + did_model.params[\"after_treatment[T.True]:treated[T.True]\"]\n",
    "\n",
    "plt.plot([pre_year, post_year], [pre_control_y, post_control_y], color=\"C0\", label=\"Control\")\n",
    "plt.plot([pre_year, post_year], [pre_treat_y, post_treat_y0], color=\"C1\", ls=\"dashed\")\n",
    "plt.plot([pre_year, post_year], [pre_treat_y, post_treat_y1], color=\"C1\", label=\"California\")\n",
    "\n",
    "plt.vlines(x=1988, ymin=40, ymax=140, linestyle=\":\", lw=2, label=\"Proposition 99\", color=\"black\")\n",
    "plt.title(\"DiD Estimation\")\n",
    "plt.ylabel(\"Cigarette Sales\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f4a1b5d",
   "metadata": {},
   "source": [
    "This estimate should be taken with a grain of salt, though. We know that Diff-in-Diff requires the trend in the control group to be equal to that of the treated group in the absence of the treatment. Formally, $E[Y(0)_{post, co} - Y(0)_{pre, co}] = E[Y(0)_{post, tr} - Y(0)_{pre, tr}]$. This is an untestable assumption, but looking at the pre-treatment trend of California (the treated unit) and the other states, we can get a feeling for how plausible it is. Specifically, we can see that the trend in `cigsale` for California is not parallel to the other states, at least in the pre-treatment periods. Cigarette sales in California are decreasing faster than the average of the control states, even prior to the treatment. If this trend extends beyond the pre-treatment period, the DiD estimator will be downward biased, meaning that the true effect is actually less extreme than the one we've estimated above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5a8a2827",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:19.447291Z",
     "start_time": "2023-07-27T13:05:19.285436Z"
    },
    "scrolled": true,
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(data_piv.drop(columns=[\"california\"]), color=\"C1\", alpha=0.3)\n",
    "plt.plot(data_piv.drop(columns=[\"california\"]).mean(axis=1), lw=3, color=\"C1\", ls=\"dashed\", label=\"Control Avg.\")\n",
    "plt.plot(data_piv[\"california\"], color=\"C0\", label=\"California\")\n",
    "plt.vlines(x=1988, ymin=40, ymax=300, linestyle=\":\", lw=2, label=\"Proposition 99\", color=\"black\")\n",
    "plt.legend()\n",
    "plt.ylabel(\"Cigarette Sales\")\n",
    "plt.title(\"Non-Parallel Trends\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d354b70",
   "metadata": {},
   "source": [
    "The problem of non-parallel trends is where Synthetic Control comes into play in the Synthetic Diff-in-Diff model. But we are getting ahead of ourselves. Regardless of DiD being a valid model for the data above, what is interesting about it is that we can recast it into the Two-Way Fixed-Effects formulation. To frame DiD like this, we fit unit ($\\alpha_i$) and time ($\\beta_t$) averages, alongside the treatment indicator.\n",
    " \n",
    "$$\n",
    "\\hat{\\tau}^{did} = \\underset{\\mu, \\alpha, \\beta, \\tau}{argmin} \\bigg\\{ \\sum_{i=1}^N \\sum_{t=1}^T \\big(Y_{it} - (\\mu + \\alpha_i + \\beta_t + \\tau D_{it}\\big)^2 \\bigg\\}\n",
    "$$\n",
    " \n",
    "In this formulation, the unit effects capture the difference in intercepts for each unit while the time effects capture the general trend across both treated and control units. To implement this, we could either add time and unit dummies to the model or demean the data. In this process, we subtract the average across both time and units from both treatment and outcome variables:\n",
    " \n",
    "$$\n",
    "\\ddot{Y}_{it} = Y_{it} - \\bar{Y}_i  - \\bar{Y}_t\\\\\n",
    "\\ddot{D}_{it} = D_{it} - \\bar{D}_i - \\bar{D}_t\n",
    "$$\n",
    " \n",
    "Where, $\\bar{X}_i$ is the average across all time periods for unit $i$ and $\\bar{X}_t$ is the average across all units for time $t$: \n",
    " \n",
    "$$\n",
    "\\ddot{Y}_{it} = Y_{it} - T^{-1}\\sum_{t=0}^{t=T} Y_{it}  - N^{-1}\\sum_{i=0}^{i=N} Y_{it}\\\\\n",
    "\\ddot{D}_{it} = D_{it} - T^{-1}\\sum_{t=0}^{t=T} D_{it} - N^{-1}\\sum_{i=0}^{i=N} D_{it}\n",
    "$$\n",
    " \n",
    "After demeaning, a simple regression of the outcome on the treatment indicator (`treat*post`) yields the difference in difference estimator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8ab9bb9c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:19.468356Z",
     "start_time": "2023-07-27T13:05:19.448700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td> -119.1647</td> <td>    0.333</td> <td> -358.379</td> <td> 0.000</td> <td> -119.817</td> <td> -118.512</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>treat</th>     <td>  -27.3491</td> <td>    4.283</td> <td>   -6.385</td> <td> 0.000</td> <td>  -35.753</td> <td>  -18.945</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@curry\n",
    "def demean(df, col_to_demean):\n",
    "    return df.assign(**{col_to_demean: (df[col_to_demean]\n",
    "                                        - df.groupby(\"state\")[col_to_demean].transform(\"mean\")\n",
    "                                        - df.groupby(\"year\")[col_to_demean].transform(\"mean\"))})\n",
    "\n",
    "formula = f\"\"\"cigsale ~ treat\"\"\"\n",
    "mod = smf.ols(formula,\n",
    "              data=data\n",
    "              .assign(treat = data[\"after_treatment\"]*data[\"treated\"])\n",
    "              .pipe(demean(col_to_demean=\"treat\"))\n",
    "              .pipe(demean(col_to_demean=\"cigsale\")))\n",
    "\n",
    "mod.fit().summary().tables[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0b13791",
   "metadata": {},
   "source": [
    "As you can see, we get the exact same parameter as before. After all, both approaches are simply different ways of looking at the same DiD estimator. However, the reason this formulation is much more interesting for our purpose is that it allows us to see how DiD is actually quite similar to Synthetic Controls. Take a very close look at the TWFE formulation above. Notice that it is a regression problem with time effects and unit effects. But notice how there are no weights in the optimization objective. That is the main difference between Diff-in-Diff and Synthetic Controls, as we will see shortly. \n",
    " \n",
    "## Synthetic Controls Revisited\n",
    " \n",
    "In the canonical Synthetic Control estimator, we find unit (state) weights that minimize the difference between the pre-treated outcome of the treated unit and the weighted average of the pre-treated outcome of the control units (in a setting with no covariates). We also constrain the weights to be all positive and sum up to one. To find those weights, we solve the following optimization problem:\n",
    " \n",
    "$$\n",
    "\\hat{w}^{sc} = \\underset{w}{\\mathrm{argmin}} \\ ||\\pmb{\\bar{y}}_{pre, tr} - \\pmb{Y}_{pre, co} \\pmb{w}_{co}||^2_2 \\\\\n",
    "\\text{s.t } \\ \\sum w_i = 1 \\text{ and } \\ w_i > 0 \\ \\forall \\ i\n",
    "$$\n",
    " \n",
    "where the outcome $\\pmb{Y}_{pre, co}$ is a $T_{pre}$ by $N_{co}$ matrix, where the columns are the units and the rows are the time periods. $\\pmb{w}_{co}$ is a $N_{co}$ by 1 column vector, with one entry for each unit. Finally, $\\pmb{\\bar{y}}_{pre, tr}$ is a $T_{pre}$ by 1 column vector, where each entry is the time average of the treated units in the pre-treatment period. This is why we sometimes call Synthetic Control a horizontal regression. In most regression problems, the units are the rows of the matrix, but here they are the columns. Hence, we are regressing the average outcome of the treated units on the control units.\n",
    " \n",
    "Once we find the weights that solve the problem above, we can multiply them by the control units at all time periods to get a synthetic control for the treated unit:\n",
    " \n",
    "$$\n",
    "\\pmb{y}_{sc} = \\pmb{Y}_{co}\\hat{\\pmb{w}}^{sc}\n",
    "$$\n",
    " \n",
    "The idea here is that $\\pmb{y}_{post, sc}$ is a good estimator for our missing potential outcome $Y(0)_{post, tr}$. If that is the case, the $ATT$ is simply the average of the treated unit in the post-treatment period minus the average of the synthetic control, also in the post treatment period.\n",
    " \n",
    "$$\n",
    "\\hat{\\tau} =  \\bar{y}_{post, tr} - \\bar{y}_{post, sc}\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b91d9890",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:19.589722Z",
     "start_time": "2023-07-27T13:05:19.472499Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SC ATT:  -19.5136\n"
     ]
    }
   ],
   "source": [
    "from sc import SyntheticControl\n",
    "\n",
    "sc_model = SyntheticControl()\n",
    "\n",
    "y_co_pre = data.query(\"~after_treatment\").query(\"~treated\").pivot(\"year\", \"state\", \"cigsale\")\n",
    "y_tr_pre = data.query(\"~after_treatment\").query(\"treated\")[\"cigsale\"]\n",
    "\n",
    "sc_model.fit(y_co_pre, y_tr_pre)\n",
    "sc_weights = pd.Series(sc_model.w_, index=y_co_pre.columns, name=\"sc_w\")\n",
    "\n",
    "sc = data.query(\"~treated\").pivot(\"year\", \"state\", \"cigsale\").dot(sc_weights)\n",
    "\n",
    "att = data.query(\"treated\")[\"cigsale\"][sc.index > 1988].mean() - sc[sc.index > 1988].mean()\n",
    "\n",
    "print(\"SC ATT: \", att.round(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1066f7e",
   "metadata": {},
   "source": [
    "This estimate is much smaller than the one we got with Diff-in-Diff. Synthetic Controls can accommodate non-parallel pre-treatment trends much better, so it is not susceptible to the same bias as Diff-in-Diff. Rather, the process of baking a Synthetic Control enforces parallel trends, at least in the pre-treatment period. As a result, the estimate we get is much smaller and much more plausible. \n",
    " \n",
    "We can visualize this estimation process by plotting the realized outcome for California alongside the outcome of the synthetic control. We also plot as dashed lines the post intervention average of both California and the synthetic control. The difference between these lines is the estimated $ATT$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fd943e96",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:19.680802Z",
     "start_time": "2023-07-27T13:05:19.591541Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sc, label=\"Synthetic Control\")\n",
    "plt.plot(sc.index, data.query(\"treated\")[\"cigsale\"], label=\"California\", color=\"C1\")\n",
    "\n",
    "calif_avg = data.query(\"treated\")[\"cigsale\"][sc.index > 1988].mean()\n",
    "sc_avg = sc[sc.index > 1988].mean()\n",
    "\n",
    "plt.hlines(calif_avg, 1988, 2000, color=\"C1\", ls=\"dashed\")\n",
    "plt.hlines(sc_avg, 1988, 2000, color=\"C0\", ls=\"dashed\")\n",
    "\n",
    "plt.title(\"SC Estimation\")\n",
    "plt.ylabel(\"Cigarette Sales\")\n",
    "plt.vlines(x=1988, ymin=40, ymax=140, linestyle=\":\", lw=2, label=\"Proposition 99\", color=\"black\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aca0d0d5",
   "metadata": {},
   "source": [
    "Interestingly enough, we can also recast the Synthetic Control estimator as solving the following optimization problem, which is quite similar to the Two-Way Fixed-Effects formulation we used for Diff-in-Diff\n",
    " \n",
    "$$\n",
    "\\hat{\\tau}^{sc} = \\underset{\\beta, \\tau}{argmin}  \\bigg\\{ \\sum_{i=1}^N \\sum_{t=1}^T \\big(Y_{it} - \\beta_t - \\tau D_{it}\\big)^2 \\hat{w}^{sc}_i \\bigg\\}\n",
    "$$\n",
    " \n",
    "where the $\\hat{w}^{sc}_i$ weights for the control units are estimated from the optimization problem we saw earlier. For the treated unit,  the weights are simply $1/N_{tr}$ (uniform weighting). \n",
    " \n",
    "Notice the difference between SC and DiD here. First, Synthetic Control adds unit weights $\\hat{w}^{sc}_i$ to the equation. Second, we have time fixed effects $\\beta_t$ but no unit fixed effect $\\alpha_i$, nor an overall intercept  $\\mu$. \n",
    " \n",
    "To verify that these two formulations are actually equivalent, here is the code for it, which yields the exact same $ATT$ estimate. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "529e70b3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:19.742761Z",
     "start_time": "2023-07-27T13:05:19.682537Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>treat</th> <td>  -19.5136</td> <td>   13.289</td> <td>   -1.468</td> <td> 0.142</td> <td>  -45.586</td> <td>    6.559</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@curry\n",
    "def demean_time(df, col_to_demean):\n",
    "    return df.assign(**{col_to_demean: (df[col_to_demean]\n",
    "                                        - df.groupby(\"year\")[col_to_demean].transform(\"mean\"))})\n",
    "\n",
    "data_w_cs_weights = data.set_index(\"state\").join(sc_weights).fillna(1/len(sc_weights))\n",
    "\n",
    "formula = f\"\"\"cigsale ~ -1 + treat\"\"\"\n",
    "\n",
    "mod = smf.wls(formula,\n",
    "              data=data_w_cs_weights\n",
    "              .assign(treat = data_w_cs_weights[\"after_treatment\"]*data_w_cs_weights[\"treated\"])\n",
    "              .pipe(demean_time(col_to_demean=\"treat\"))\n",
    "              .pipe(demean_time(col_to_demean=\"cigsale\")),\n",
    "              weights=data_w_cs_weights[\"sc_w\"]+1e-10)\n",
    "\n",
    "mod.fit().summary().tables[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df1dae26",
   "metadata": {},
   "source": [
    "We just saw how the two approaches, SC and DiD, are actually closely related. Now, we are ready to talk about Synthetic Diff-in-Diff. As you can probably guess, we will just add weights to the DiD estimator or unit fixed-effects to the Synthetic Control estimator. \n",
    " \n",
    "![img](data/img/sdid/both-pills.png)\n",
    " \n",
    "## Synthetic Diff-in-Diff\n",
    " \n",
    "Before we jump right into the Synthetic Diff-in-Diff estimator, let me just reproduce the same equations we saw earlier for SC and DiD, which will ease the comparison.\n",
    " \n",
    "$$\n",
    "\\hat{\\tau}^{sc} = \\underset{\\beta, \\tau}{argmin}  \\bigg\\{ \\sum_{i=1}^N \\sum_{t=1}^T \\big(Y_{it} - \\beta_t - \\tau D_{it}\\big)^2 \\hat{w}^{sc}_i \\bigg\\}\n",
    "$$\n",
    " \n",
    "$$\n",
    "\\hat{\\tau}^{did} = \\underset{\\mu, \\alpha, \\beta, \\tau}{argmin} \\bigg\\{ \\sum_{i=1}^N \\sum_{t=1}^T \\big(Y_{it} - (\\mu + \\alpha_i + \\beta_t + \\tau D_{it}\\big)^2 \\bigg\\}\n",
    "$$\n",
    " \n",
    "Next, just like I promised, we can easily merge the equations above into one which will contain elements from both of them:\n",
    " \n",
    "$$\n",
    "\\hat{\\tau}^{sdid} = \\underset{\\mu, \\alpha, \\beta, \\tau}{argmin}  \\bigg\\{ \\sum_{i=1}^N \\sum_{t=1}^T \\big(Y_{it} - (\\mu + \\alpha_i + \\beta_t + \\tau D_{it})^2 \\hat{w}^{sdid}_i \\hat{\\lambda}^{sdid}_t \\big) \\bigg\\}\n",
    "$$\n",
    " \n",
    "As you can see, we've added back the $\\alpha_i$ unit fixed effects. We've also kept the unit weights $\\hat{w}_i$. But there is something new, which is the time weights $\\hat{\\lambda}_t$. Don't worry. There is nothing fancy about them. Remember how the unit weights $w_i$ minimized the difference between the control units and the average of treated units? In other words, we use them to match the pre-trend of the treated and control groups. The time weight does the same thing, but for the periods. That is, it minimizes the difference between the pre and post-treated periods for the controls. \n",
    " \n",
    "$$\n",
    "\\hat{\\lambda}^{sdid} = \\underset{\\lambda}{\\mathrm{argmin}} \\ ||\\bar{\\pmb{y}}_{post, co} - (\\pmb{\\lambda}_{pre} \\pmb{Y}_{pre, co} +  \\lambda_0)||^2_2 \\\\\n",
    "\\text{s.t } \\ \\sum \\lambda_t = 1 \\text{ and } \\ \\lambda_t > 0 \\ \\forall \\ t\n",
    "$$\n",
    " \n",
    "Again, $\\pmb{Y}_{pre, co}$ is a $T_{pre}$ by $N_{co}$ matrix of outcomes where the rows represent time periods and the columns represent the units. But now $\\bar{\\pmb{y}}_{post, co}$ is a 1 by $N_{co}$ row vector, where each entry is the time average outcome for that control unit in the post-treatment period. Finally, $\\pmb{\\lambda}_{pre}$ is a 1 by $T_{pre}$ row vector, with one entry for each pretreatment period. Another way to see this is by noticing that the unit weights $w$ were post-multiplying the outcome matrix $\\pmb{Y}_{pre, co} \\pmb{w}_{co}$. This means we were regressing the average outcome **for each unit** of the treated group on the outcome of the units in the control group. Now, we are flipping that problem on its head, regressing the average outcome **for each post-treatment time period** of the control group on the outcome of the same control units, but in the pre-treatment period.\n",
    " \n",
    "As for the time weights in the post-treated periods, we just set them to one over the number of post-treated periods $1/T_{post}$ (again, doing uniform weighting). Notice that we also have an intercept $\\lambda_0$. We do this to allow the post-treatment period to be above or below all the pre-treatment periods, which is the case in many applications with a clear positive or negative trend.\n",
    " \n",
    "If all of this seems a bit abstract, maybe code will help you understand what is going on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4667b8f3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:19.748944Z",
     "start_time": "2023-07-27T13:05:19.744139Z"
    }
   },
   "outputs": [],
   "source": [
    "def fit_time_weights(data, outcome_col, year_col, state_col, treat_col, post_col):\n",
    "        \n",
    "        control = data.query(f\"~{treat_col}\")\n",
    "        \n",
    "        # pivot the data to the (T_pre, N_co) matrix representation\n",
    "        y_pre = (control\n",
    "                 .query(f\"~{post_col}\")\n",
    "                 .pivot(year_col, state_col, outcome_col))\n",
    "        \n",
    "        # group post-treatment time period by units to have a (1, N_co) vector.\n",
    "        y_post_mean = (control\n",
    "                       .query(f\"{post_col}\")\n",
    "                       .groupby(state_col)\n",
    "                       [outcome_col]\n",
    "                       .mean()\n",
    "                       .values)\n",
    "        \n",
    "        # add a (1, N_co) vector of 1 to the top of the matrix, to serve as the intercept.\n",
    "        X = np.concatenate([np.ones((1, y_pre.shape[1])), y_pre.values], axis=0)\n",
    "        \n",
    "        # estimate time weights\n",
    "        w = cp.Variable(X.shape[0])\n",
    "        objective = cp.Minimize(cp.sum_squares(w@X - y_post_mean))\n",
    "        constraints = [cp.sum(w[1:]) == 1, w[1:] >= 0]\n",
    "        problem = cp.Problem(objective, constraints)\n",
    "        problem.solve(verbose=False)\n",
    "        \n",
    "        # print(\"Intercept: \", w.value[0])\n",
    "        return pd.Series(w.value[1:], # remove intercept\n",
    "                         name=\"time_weights\",\n",
    "                         index=y_pre.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21a3827c",
   "metadata": {},
   "source": [
    "The first thing we do in this code is to filter out the treated group. Then, we pivot the pre-treated data so that we have the matrix $\\pmb{Y}_{pre,co}$. Next, we group the post-treatment data to get the average outcome for each control unit in the post-treatment period. We then add a row full of ones to the top of $\\pmb{Y}_{pre,co}$, which will serve as the intercept. Finally, we regress $\\bar{\\pmb{y}}_{post, co}$ on the pre-treated periods (the rows of $\\pmb{Y}_{pre,co}$) to get the time weights $\\lambda_t$. Notice how we add the constraints to have the weights sum up to 1 and be non-negative. Finally, we toss the intercept away and store the time weights in a series. \n",
    " \n",
    "Here is the result we get by running the code above to find the time weights in the Proposition 99 problem. Notice that all periods except for 1986, 87 ans 88 get zero weights. This means that a weighted average of only the last 3 periods is enough to balance pre and post treatment periods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e9353abe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:19.767415Z",
     "start_time": "2023-07-27T13:05:19.750660Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1984   -0.000\n",
       "1985   -0.000\n",
       "1986    0.366\n",
       "1987    0.206\n",
       "1988    0.427\n",
       "Name: time_weights, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_weights = fit_time_weights(data,\n",
    "                                outcome_col=\"cigsale\",\n",
    "                                year_col=\"year\",\n",
    "                                state_col=\"state\",\n",
    "                                treat_col=\"treated\",\n",
    "                                post_col=\"after_treatment\")\n",
    "\n",
    "time_weights.round(3).tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ac461a9",
   "metadata": {},
   "source": [
    "To understand a bit more about the role of these weights, we can plot $\\hat{\\pmb{\\lambda}}_{pre} \\pmb{Y}_{pre, co} +  \\hat{\\lambda}_0$ as a horizontal line in the pretreatment period that doesn't get zeroed out. Next to it, we plot the average outcome in the post-treatment period. Notice how they align perfectly. We also show the estimated time weights in red bars and in the secondary axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "44cc1d24",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:20.068272Z",
     "start_time": "2023-07-27T13:05:19.769240Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.plot(data.query(\"~treated\").query(\"~after_treatment\").groupby(\"year\")[\"cigsale\"].mean())\n",
    "ax.plot(data.query(\"~treated\").query(\"after_treatment\").groupby(\"year\")[\"cigsale\"].mean())\n",
    "\n",
    "intercept = -15.023877689807628\n",
    "ax.hlines((data.query(\"~treated\").query(\"~after_treatment\").groupby(\"year\")[\"cigsale\"].mean() * time_weights).sum() - 15, 1986, 1988,\n",
    "          color=\"C0\", ls=\"dashed\", label=\"\"\" $\\lambda_{pre} Y_{pre, co} + \\lambda_0$\"\"\")\n",
    "ax.hlines(data.query(\"~treated\").query(\"after_treatment\").groupby(\"year\")[\"cigsale\"].mean().mean(), 1988, 2000,\n",
    "          color=\"C1\", ls=\"dashed\", label=\"\"\"Avg  $Y_{post, co}$\"\"\")\n",
    "ax.vlines(x=1988, ymin=90, ymax=140, linestyle=\":\", lw=2, label=\"Proposition 99\", color=\"black\")\n",
    "plt.legend()\n",
    "\n",
    "plt.title(\"Time Period Balancing\")\n",
    "plt.ylabel(\"Cigarette Sales\");\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "ax2.bar(time_weights.index, time_weights, label=\"$\\lambda$\")\n",
    "ax2.set_ylim(0,10)\n",
    "ax2.set_ylabel(\"Time Weights\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b15ee07d",
   "metadata": {},
   "source": [
    "Now that we learned what are the time weights $\\lambda_t$ in the Synthetic Diff-in-Diff estimator and how to estimate them, let's turn our attention to the unit weights $w_i$. And no, unfortunately they are not just like the ones we get when using traditional Synthetic Controls. The first difference between them is that we also allow for an intercept $w_0$. We do this because we don't need the treated unit and synthetic control to have the same level anymore. Since we will throw DiD into the mix, we only need to make the synthetic control and treated unit have parallel trends.\n",
    " \n",
    "The next difference is that we add a $L_2$ penalty to the weights. This helps non-zero weights to be more distributed across the control units, as opposed to having just a few of them contributing to the synthetic control. The $L_2$ penalty ensures we don't have very big weights, which forces us to use more units. \n",
    " \n",
    " \n",
    "$$\n",
    "\\hat{w}^{sdid} = \\underset{w}{\\mathrm{argmin}} \\ ||\\bar{\\pmb{y}}_{pre, tr} - (\\pmb{Y}_{pre, co} \\pmb{w}_{co} +  w_0)||^2_2 + \\zeta^2 T_{pre} ||\\pmb{w}_{co}||^2_2\\\\\n",
    "\\text{s.t } \\ \\sum w_i = 1 \\text{ and } \\ w_i > 0 \\ \\forall \\ i\n",
    "$$\n",
    "\n",
    "\n",
    "There is also this $\\zeta^2$ term, which is theoretically motivated, but very complicated to explain, so I will unfortunately leave it as a bit of a mystery. In the reference, you can check the original article, which explains them. We define it like this:\n",
    " \n",
    "$$\n",
    "\\zeta = (N_{tr}* T_{post})^{1/4}\\sigma(\\Delta_{it})\n",
    "$$\n",
    " \n",
    "where $\\Delta_{it}$ is the first difference in the outcomes $Y_{it} - Y_{i(t-1)}$ and $\\sigma(\\Delta_{it})$ is the standard deviation of this difference. Here is the code to compute it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b6345288",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:20.072764Z",
     "start_time": "2023-07-27T13:05:20.069640Z"
    }
   },
   "outputs": [],
   "source": [
    "def calculate_regularization(data, outcome_col, year_col, state_col, treat_col, post_col):\n",
    "    \n",
    "    n_treated_post = data.query(post_col).query(treat_col).shape[0]\n",
    "    \n",
    "    first_diff_std = (data\n",
    "                      .query(f\"~{post_col}\")\n",
    "                      .query(f\"~{treat_col}\")\n",
    "                      .sort_values(year_col)\n",
    "                      .groupby(state_col)\n",
    "                      [outcome_col]\n",
    "                      .diff()\n",
    "                      .std())\n",
    "    \n",
    "    return n_treated_post**(1/4) * first_diff_std"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91eadd6b",
   "metadata": {},
   "source": [
    "As for the unit weights, there is nothing particularly new in them. We can reuse a lot of the code from the function to estimate the time weights. We only need to be careful about the dimensions, since the problem is now upside down."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "700298fb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:20.079312Z",
     "start_time": "2023-07-27T13:05:20.074258Z"
    }
   },
   "outputs": [],
   "source": [
    "def fit_unit_weights(data, outcome_col, year_col, state_col, treat_col, post_col):\n",
    "    \n",
    "    zeta = calculate_regularization(data, outcome_col, year_col, state_col, treat_col, post_col)\n",
    "    pre_data = data.query(f\"~{post_col}\")\n",
    "    \n",
    "    # pivot the data to the (T_pre, N_co) matrix representation\n",
    "    y_pre_control = (pre_data\n",
    "                     .query(f\"~{treat_col}\")\n",
    "                     .pivot(year_col, state_col, outcome_col))\n",
    "    \n",
    "    # group treated units by time periods to have a (T_pre, 1) vector.\n",
    "    y_pre_treat_mean = (pre_data\n",
    "                        .query(f\"{treat_col}\")\n",
    "                        .groupby(year_col)\n",
    "                        [outcome_col]\n",
    "                        .mean())\n",
    "    \n",
    "    # add a (T_pre, 1) column to the begining of the (T_pre, N_co) matrix to serve as intercept\n",
    "    T_pre = y_pre_control.shape[0]\n",
    "    X = np.concatenate([np.ones((T_pre, 1)), y_pre_control.values], axis=1) \n",
    "    \n",
    "    # estimate unit weights. Notice the L2 penalty using zeta\n",
    "    w = cp.Variable(X.shape[1])\n",
    "    objective = cp.Minimize(cp.sum_squares(X@w - y_pre_treat_mean.values) + T_pre*zeta**2 * cp.sum_squares(w[1:]))\n",
    "    constraints = [cp.sum(w[1:]) == 1, w[1:] >= 0]\n",
    "    \n",
    "    problem = cp.Problem(objective, constraints)\n",
    "    problem.solve(verbose=False)\n",
    "    \n",
    "    # print(\"Intercept:\", w.value[0])\n",
    "    return pd.Series(w.value[1:], # remove intercept\n",
    "                     name=\"unit_weights\",\n",
    "                     index=y_pre_control.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e8750fa",
   "metadata": {},
   "source": [
    "First, we calculate $\\zeta$ using the function we defined earlier and filter out the post-treatment period. Next, we pivot the pre-treatment data to get the $\\bar{\\pmb{y}}_{pre, tr}$ matrix of outcomes. Then, we add a column full of ones to the beginning of the $\\bar{\\pmb{y}}_{pre, tr}$ matrix. This column will allow us to estimate the intercept. With all of that, we define the optimization objective, which includes the $L_2$ penalty on the weights. Finally, we toss the intercept away and store the estimated weights in a series.\n",
    " \n",
    "If we use this code to estimate the unit weights in the Proposition 99 problem, here is the result we get for the first 5 states:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d6bdd436",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:20.131686Z",
     "start_time": "2023-07-27T13:05:20.080798Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "1   -0.000\n",
       "2   -0.000\n",
       "4    0.057\n",
       "5    0.078\n",
       "6    0.070\n",
       "Name: unit_weights, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unit_weights = fit_unit_weights(data,\n",
    "                                outcome_col=\"cigsale\",\n",
    "                                year_col=\"year\",\n",
    "                                state_col=\"state\",\n",
    "                                treat_col=\"treated\",\n",
    "                                post_col=\"after_treatment\")\n",
    "\n",
    "unit_weights.round(3).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cbd2cb6",
   "metadata": {},
   "source": [
    "These unit weights also define a synthetic control that we can plot alongside the outcome of California. We'll also plot the traditional synthetic control we've estimated earlier alongside the one we've just estimated plus the intercept term. This will give us some intuition on what is going on and the difference between what we just did and traditional Synthetic Control."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d3b47ac1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:20.316076Z",
     "start_time": "2023-07-27T13:05:20.135638Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YjP9qLRYLZrPZzVF5FqkTV5dSJ3a7nZycHPz8/Co5KiGEEJeqZLvAXeSz15XUiavqbI/I9A0hhKgiDofD7Q0PUbtYLBYcDoe7wxBCCFEKaReI2qKi7RHplBBCiCoiQ+iFO8jfnRBCeCb5/1nUJhX5e6+WTgmr1fqh1WpNsVqt20ps/7vVat1ltVq3W63WF4ttf9hqte51vjesOmIUQojL0WuvvcaAAQMYPHgwQ4YMYePGjRd1nPnz57N79+6i1+PHj2fz5s3l3v/IkSN8//33Ra83b97MtGnTyr1/fn4+zz77LL169WLgwIGMGjWKpUuXlnv/4kpeS3md75pvvfVWDh06BMBXX33FoEGDGDx4MAMHDmTBggUA3H333XTv3p3BgwfTu3dv7rrrLo4fP150jG7dunHq1CkAEhISGDJkSNHv7p133im647Bz507uvvvuCscvpD0ihBBCeKLqGinxETC8+Aar1ToAGAu0s9lsrYH/OLe3Aq4FWjv3+Z/VapUJPkIIUUHr169n8eLFzJ8/n8WLFzNz5kxiY2Mv6lgX+0W+UMlOifbt2/PUU0+Ve/+XXnqJ5ORkli5dytKlS/noo484e/bsRcVyvmux2+0VPt6uXbtwOBzUr1+fY8eO8frrr/P999+zePFifvzxR1q2bFlU9rHHHmPx4sWsWLGCNm3aMGHCBPLy8lyO6evry6JFi1i2bBlfffUVS5cuZfr06QC0bNmS48ePk5iYWOFYhbRHhBBCCE9TLZ0SNpttOXCqxOY7gOdtNluus0yKc/tY4CubzZZrs9kOAHuBrtURpxBCXE5SUlIICwvDx8cHgLCwMGJiYlixYgU333xzUbnly5dzyy23ANC0aVOef/55Bg8ezOjRozlx4gTr1q1j0aJFPP300wwZMoSDBw8CMGfOHEaNGkXv3r1Zs2YNAAUFBTz11FOMHDmSwYMH8+mnnwLw7LPPsnbtWoYMGcK7777L6tWrufHGGwHIzMzknnvuKRpdMHfu3HOuIzs7m88//5ynn3666FoiIyO58sorAZg1axaDBg1i4MCBPPPMM0X7lfdaxo8fz3PPPcc111zD+++/z4oVKxg6dCiDBg3i3nvvvWDm6e+++46hQ4cCkJqaSkBAAAEBAQAEBARQr149l32UUtx2221ERUWxbNmy8x4/IiKCF198kRkzZqC1BmDIkCHMnj37vPsJV9IeEUIIeOWVVy55FGVaWhofffRR0esjR44wcODASzpG4ed6eaWkpHDHHXfQs2dP+vfvzw033MC+ffsqdIzCOGbMmFHh/cBoa5QmOzuba665hoKCAuD8I1fPNzqy+PHPVy4vL4+rr776om6ueAJ3ZlppBvSxWq3PADnA/TabbR0QB/xWrNxR5zYXVqv1NuA2AJvNRkRERKUGaLFYKv2YNZ3UiSupk9JJvRhLKZVMaFWdCa4GDRrEq6++Sp8+fejbty9jx44t+uB+7LHHOHPmDBEREdhsNiZNmoTFYiErK4suXbrw2GOP8eSTT/Lll19y7733MmzYMIYMGcKYMWMA40u1w+FgwYIFLF68mFdeeYVvvvmGL774gpCQEBYuXEhubi5jxoxh4MCBTJs2jf/97398/vnnAKxataporuHrr79OSEgIv/zyCwBnzpw5p54OHz5MfHw8derUcbnGpKQknn32WRYuXEhoaChWq5WFCxcycuTICl3L2bNnmT17Njk5OfTo0YNvvvmGxo0bc+edd/LZZ59x++23o5TCbDa7/A43bNjANddcg8VioV27dkRFRdGjRw/69OnDyJEjGTbMGPVvMplc9m/Xrh379+/HYrEUHb9Q8XKNGzdGa83p06eJioqiY8eOvP7669x1110udeLj41Pr/+1VkLRHaiCpE1dSJ648rU5KaxdUt8LO+cWLF+Pj40Nqair5+fkVjiszM5NPPvmk6KZG4edXRY5T8hg//fRTuffVWnPLLbcwceJE3nvvPQC2bdvG6dOnL+paZsyYwdSpU0s9T+ESl2Up7Xxff/01o0aNwsfHh3Xr1rFkyZIy69zX17foBsWJEye44447yMzM5IEHHjjn+OcrZ7FY6Nu3L3PmzGH8+PFlxrpq1SpmzpzJ66+/Xq66uZS/14q0R9z5r8IC1AG6A10Am9VqbQSUlhFDl3YAm832LvBuYZmTJ09WaoARERFU9jFrOqkTV1InpfP0ehk+3BjBPX/+/Co7R25ubtGHtGPh96iU4+jKXBkhJg7T0HFlvu3j48O8efNYs2YNq1ev5rbbbuPhhx9m4sSJXH311dhsNiZOnMj69et59dVXsdvteHt7M3DgQOx2O61bt2bFihXY7XYcDgcFBQVFPfBaa4YPH15U7siRI9jtdpYtW8bOnTv58ccfAcjIyGDv3r14eXmhtS7av6CgoOiu/y+//ML//ve/ovcCAwPP6ekvLFta7/+GDRvo3r07oaGhAIwbN47Vq1czdOjQCl3L6NGjsdvt7Nq1i4SEBOrXr4/dbueaa67h448/5uabb0Zrfc5+hZKSkggNDS3a/tlnn7Fp0yZWrlzJv/71LzZt2sR9993ncl4wMrE7HA7sdnvR8QuVPE/x84eGhpKUlFRqneTm5rr827vYaTu1hLRHaiCpE1dSJ648rU6KtwsKFU4nbNasWbXEcPz4ccLCwjCbzdjtdkJCQgBjRGNYWFhRB8Hzzz9PZGQkQ4cO5frrr6dr166sX7+emJgYPvzwQ5566ikOHTrEgAED6Nu3LzfddBMFBQXcc88955Tz8/Pj22+/5cMPPyQvL48rrriC5557DrPZ7HKMTz75hD179gDGl/p33nkHMKYt/ve//z3nOlauXInFYmHy5MlFn4UtWrQAjM/Pd955h5kzZwIwadIkbr31Vo4cOVLua7n++uvp2bMnGzZs4MMPP2Tu3LkuxytU2mfxN998w5tvvondbuf48ePUqVPHpc6L71f4vE6dOrzwwguMHDmSe+65p9zllFIMGTKE559/nquuuqrM339BQUFRu6Mshw8f5p577mH27Nn8/vvvDB8+nK1btxISEsKQIUOYO3duuZb6rEh7xJ2rbxwFvrPZbNpms60FHECEc3tCsXLxwDE3xCeEuIxt3bqVrVu3ujuMKmc2m+nZsyf3338/Tz/9dNFdiIkTJ/Ldd98xa9YsRo8eXdQTXnjHvnDf831oeXt7l1ru6aefZtGiRSxatIjffvuNfv36nTdGrfV5MzQ3bNiQxMTEUnNIFHZslKYi1+Lv73/B45XF19f3nCkeSimuuOIK/v73v/O///3vvHd+tm3bVubQz+IOHTqEyWQquuOQm5uLr69vhWMVpZL2iBDCbbKzs8nOzq628/Xr149jx47Ru3dvHn74YX799VfA+KL99ddfA0aH+Q8//MC4ccaNjwMHDjBlyhSWLVtGcHAwP/30E4888gj169dn0aJFRYmrSyu3Z88efvjhB2bNmsWiRYswm8189913AKUeA4xcTa+//jo2m43Fixfz5JNPulzHrl27aNu2banXuGXLFmw2G3PmzOHHH3/kiy++YNu2bRW6ln379jF+/HgWLlzIqVOnyjxeafLy8jh8+DAJCQnnrfOy1K9fH631BTvUSpZr0aIFmzZtOu8+5REcHFzU5vrwww/p2LEjaWlpLFmyhD59+pSrQ6Ki3DlSYhYwEPjZarU2A7yBk8APwBdWq3U6EAs0Bda6K0ghxOVp3rx51Xo+09BxWCyWap3rt3fvXkwmE40aNQJg+/btxMfHAxATE0N0dDSvv/46X3755QWPFRgYSGZm5gXL9evXj08++YRevXrh5eXFvn37qFu37nn379evHzNmzChqdJw5c6Zo5AOAn58fkyZNYtq0abzwwgt4e3uTnJzMypUr6d27N//61784deoUISEhzJo1i7/85S8XfS1NmjThyJEjHDhwgIYNG/Ltt9/SvXv38x6vadOmHDhwgISEBJKSkjhx4kRRQ2n79u3ExbmO+Nda8+GHH5KcnEz//v3Pe/zU1FQeeughpk6dWtTJsn//fpo3b37e/US5zULaI0KIauZY+D0kJdLS2S5wbF5x6Qe9wAhKMHIdLVq0iFWrVrF69WruuOOOolGUderUYdu2bZw4cYLWrVsTFhZGZmYmCQkJtGnTBjCmHR45coSuXV1T7JRWLj09na1btzJy5EgAcnJyLjikf9WqVYwaNYqwsDCAUqdvns/atWsZPnx40Q2HESNGsGbNGoYOHVrua4mPj6dTp07nPV7hcUo6deoUwcHBRa8DAgKYP39+0cjV4nVelvLeJClezmw24+3tzdmzZwkMDDyn3OjRo8nNzSUrK4szZ84wZMgQAB599FGXdkhwcDDZ2dmcOnWKlJQUunTpQlpaGp999hmPP/44WVlZPPzww3h7e9OjRw+uvvrqcsV6PtXSKWG1Wr8E+gMRVqv1KPA48CHwoXNZrjxgis1m08B2q9VqA3YAduD/bDZbQelHFkKIi9OuXTt3h1DlsrKyeOyxx0hPT8disdCgQQNefLFotUOuvvpqUlNTyzVkdOzYsfzzn//kgw8+4N133y2z3HXXXceRI0cYPnw4WmvCwsL48MMPadmyJWazmcGDB2O1Ws/5IP/HP/7BI488wsCBAzGZTNx7771FjZdCDzzwAC+++CIDBgzAx8cHf39/7r//fqKjo3n44YeZMGECWmsGDhxYlMPhYq7F19eX6dOnc/vtt1NQUED79u254YYbznu8QYMG8euvv9K3b1/sdjtPPvkkycnJ+Pj4EB4ezvPPP19U9umnn+bVV18lOzubjh078vXXXxeNOCkuJyeHIUOGYLfbMZvNjB8/nttuu63o/dWrVzNo0KDzxiVcSXtECOFp3JFjonAUZc+ePWnRogVff/01EydOZNKkSdhsNlJSUrj22muLyhcmmS7cNycnp9TjllZOa82ECRN4+OGHyx3fhUZQgjHdpWRi7OL7l6W811LYAXGh45Wm5AjKwnOVVuelKTk6siyllcvNzT3nGgvNmTMHMNoPNpuNV199tczjFubP+Oyzz7j22mvZs2cPO3fuxOFw0LhxY7755htGjRrF0KFD+etf/1pzOiVsNtukMt66vozyzwDPlPaeEEKI8mnXrh0//PBDme+vXbuWyZMnn7OtcC4nGL3qo0ePBqBLly78/PPPRe998803Rc/DwsKKVt8wmUw8/PDDpTY+bDbbOa979uwJGHcQXnvttfNei7e3N4899hiPPfaYy3vjxo0rGmJ6qdcC0KdPHxYuXOhyvJLlCo0aNYoJEyZw//33Ex8fXzT8taTzNQCAojoEI4t5WXJzc9m8eTNPPPHEeY8nXEl7RAjhKS40oqGq7N27F29v76KVoYqPohwxYgT/+c9/sNvtvPnmm+c9TkBAQLmW5u7duzdTp07l1ltvJSIigtOnT5OZmUl8fHyZx+jduzc333wzt956K2FhYZw+fdpltETv3r154YUX+Pzzz4vaMps2bSI7O5vu3btzzz33cOedd6K1Zv78+edN7BgQEHDe0aAVPV5oaCgFBQXk5OTg6+t73pGrJZU2OrK85U6dOkV4eDheXl5l7ldeJpOJBQsW8O2333L8+HHefvttHnnkEcDIS1KYv6NkjpSL5d70r0II4SYvv/wyAPfdd5+bI3GPwmGI//rXv9wdSo3n5+fH/fffT1JSUqlTNSpbYmIijzzyiNszuAshhLh0SUlJgDGtsjpkZWVx3333kZaW5jKK0tvbm549exISEnLBL5thYWF06dKFgQMHMmDAAG666aZSyzVr1owHHniASZMmobXGYrHwzDPPEB8f73KMQs2bN+euu+5i/PjxmEwm2rRp49Kxr5Ti/fff5/HHH+fNN9/Ex8eH+Ph4nnjiCRo1asSECRMYNWoUYOTLaNOmTZkd/he6lrZt25Z6vPPp168fa9eupW/fvhccuXqh0ZHlLbd69eoKL8taFi8vLwYNGoTFYiEoKIisrCwGDx4MQN26dTl+/Dht2rQpWpL0UqmLSerlofSxY5Wbf8rTMvZ6AqkTV1InpfP0ein88piYmFhl58jKyjpn+F9155SoCaROXF1qnZT8u4OibNfnHwsrKou0R6qB1IkrqRNXnlYnpf3/vHnzZgDat29fbXGU9TnjcDgYNmwY77zzTtFd/dqistsj27Zt45133nFZNaQq3XLLLTz00EM0adKkUo5XVp1kZWXx6KOP4uPjQ9euXcucvlGR9ojcZhFC1Er33nuvu0MQQgghRC0XHR3t7hAAY2nSKVOmMHz48FrXIVEV2rRpQ69evSgoKKi0KQ7nk5eXx7BhwyqtQ+J8/P39eeWVVyr1mNIpIYSolWrrtA0hhBBCeI7qmrZxIc2aNbvgUpWiYoonC61q3t7eTJgwodrOV9lM7g5AXP70qZPog3vQBZK0XAghhBBCCCHEn2SkhKgy2p6PXr0EvXIxOBzg64dq3hbVsj00bIaqhqFMQpRly5YtQO1YGlQIIYQQnikrKwvAZe69ELWJjJQQVUIfPYB+fzp6+UJUyw6YrpmCatISvXMTjq/ew/HKv3D8+BV67060JLkTbjBixAhGjBjh7jCqRUpKCnfccQc9e/akf//+3HDDDezbt6/M8k2bNgWMjOC33npr0fa//e1vDB48mHffffeSY9q8eTPTpk275OMIIYQQNdmePXvOWcJaiNpIRkqISqVzc9HL5qDXr4LgUEzX3oJq0goA1bI92p4P+3ehd25G/7EZvXmtMYKiWWtUyw7GCApZ5k5Ug7Zt27o7hGqhtebmm29mwoQJvPXWW4CREfrkyZM0btz4vPvGxMTw3nvvAUbHxvr161m7dm25z22328tctrJ9+/bVmmlcCCGE8ER+fn7uDkEIt5Nvf6LS6D07cMz7BtLPoLr0RvUfhfLxOaeMsnhBszaoZm2cHRS7jQ6KXdvQW9aDjy+qWRtjikejZkZ5IarA/Pnz3R1CtVi1ahVeXl7ceOONRdvatGlDZmYmVquVtLQ07HY7DzzwAMOGDTtn3yNHjjBlyhSWLl3KddddR2pqKkOGDOHpp58mICCAhx56iJycHOrXr8/LL79MaGgo48ePp1OnTqxfv54hQ4awePFirrjiClavXk1aWhovv/wy3bp1Y/Xq1bz99tt88cUX/P777zz++OPk5OTg6+vL9OnTqyV7tBBCCOFuzZo1c3cIQriddEqIS6YzM9ALvkfv2ISKiEbd9HdUfMML7md0ULRGNWttdFAc2IPeuQm9ezt663rw8UE1aoFq0BQaNIGwSJRyWdZWCHEeu3btKnVUiI+PDx988AFBQUGcOnWKMWPGMHTo0DL/jc2YMYMpU6awaNEiAAYPHsxTTz1Fjx49eOmll5g+fTpPPvkkAOnp6Xz77bcALF68GLvdzty5c1myZAnTp09n5syZ5xy7SZMmfPfdd1gsFpYvX84LL7xQNEJDCCGEEEJc3qRTQlw0rTVsXY9j4SzIy0X1HYbqOeiipl8oixc0bYVq2srIMXFwjzGCwjnVA4DAIFSDpqgGTaB+UwgNk04KUWO8vz6Zg2dyjX83laRhHV9u6Xxx65trrXn++edZs2YNSimSkpI4ceIEUVFRF9w3PT2dtLQ0evToAcCECRO4/fbbi96/8sorzyk/cuRIwEgqevTo0VKPd/fdd3PgwAGUUuTn51/UNQkhhBCibKdOnWLixIkopUhJScFsNhMWFgbA3Llz8fb2Lvexxo8fz7Rp02jfvj033HADb7zxBgDff/89N910E2Dkppo2bVql32gofu7iFi1axEsvvYTD4cBut3PzzTdzww03APD111/z1ltvobVGa821117LX//610qNS1w86ZQQF0WfSUXP/Rp9YDcqvj5q1ERUZOWss6wsFmjS0kiMqTWcOgmH9qAP7kUf2I3ettEoGFIHVb+J0UnRoAkquE6lnF/UDh07dgRg48aNbo6kajVr1oy5c+e6bP/uu+9ITU1l3rx5eHl50a1bN3JzcyvlnCUziBc2csxmM/ZSEtu+9NJL9OzZkw8++IAjR44wfvz4SolDCCGE8HQ7duwAoFWrVlV+rrCwMBYtWoTFYuGFF14gICDgnC/m58sFdT6ffvopYEz7/OSTT4o6JYrnpqpq+fn5PPjgg8yZM4fY2Fhyc3M5cuQIAEuXLuX999/niy++ICYmhpycnKIRncIzSKeEqBBdUIDjt5/Rv8wDpTANvxo69kSZqmYhF6UUhEdCeCSqY0+jk+JkMvrgXqOjYvc29JZ1RuE6EagGTVD1m0DT1i75LIQoLjk5uVrPd0vnaCwWS6lfyqtS7969eeGFF/j888+ZPHkyAJs2bSIxMZGIiAi8vLxYtWpVqSMYyhIcHExISAhr1qyhW7dufPvtt3Tv3v2iY8zIyCAmxujUtNlsF30cIYQQoqZx9+jAu+++m9DQULZt20bbtm258sorS83zlJ2dzb333suePXto0qQJOTk5Rcfo1q0b8+bN49lnn+XQoUMMGTKEvn37ctNNNxXlpsrJyeHhhx9my5YtmM1mHn/8cXr16sXMmTNZtGgR2dnZHDx4kBEjRvDYY48B8NBDD7F582ZycnIYNWoU999/f5nXcfbsWex2O3XqGDcpfXx8ivJTvfHGG0ybNq2oreHr61vUJhKeQTolaimdmwP5eWDPB7vd+cgr9jy/2M98Y0qFPZ/0Q3vQB/aimrZCjbim2kcnKKUgMsYYldGlN9rhgJTj6EN74eBe9I5N6N9/g6BgTANHQ+uOVdZhImq2DRs2uDuEaqGU4v333+fxxx/nzTffxMfHh/j4eO677z6mTZvGiBEjaN26dYUTS7766qtFiS7r1avH9OnTLzrGO+64g7vvvpt3332XXr16XfRxhBBCiJqmOkZIXMj+/fuZOXMmZrOZjIyMUvM8ffLJJ/j5+bF48WJ27NjB8OHDXY7zyCOPsGvXrqL8U4UjFQA++ugjAJYsWcLevXuZNGkSK1asAGD79u0sWLAAb29v+vbty9SpU4mLi+PBBx+kTp06FBQUMHHiRHbs2FFmfdWpU4ehQ4fSrVs3evfuzeDBg7nqqqswmUzs2rWLdu3aVXKticoknRIeQp9IMjoJgkIgIKhSvkhrrSH9jDGy4GQyOB/6ZDJkZ13UMR11wjCNuwFadfCIfA7KZIKYOFRMHHTrZ3RSHNmPXvIjjtlfoNatgKHjUPEN3B2q8DCFveW1QUxMDO+8847L9h9//LHU8oXrpSckJLB06VKX52Cs4DFnzhyXfb/55psyX4eFhbFmzRoAevbsSc+ePQHo3LkzK1euLCr3wAMPlOu6hBBCiJqs4KVHKGzxFzh/qs69MQ0Yic7NxfH6Ey77qJ6DMPUahM5Ix/H28+e8Z/7nsxcVx+jRozGbzUDZeZ7WrFnDX/7yF8DoSGnZsmWFzrFu3TqmTp0KGAmu4+Pj2b9/P2CM6gwODgaMaaeJiYnExcXx448/8vnnn1NQUEBycjJ79uw5byfOf/7zH3bu3MmKFSt4++23Wb58Oa+++mqF4hTuIZ0SbqbzctFL5qA3rPpzo1IQGIwKCoagUAgKhqAQVFCI0WkRFAJBoUXTE7SjAE6fcnY4JDk7H1LQqcmQl/fncf38jdUxWrQzpjr4+ILFAhYv46fZAl7exZ57/fnTYjwPjY4mNTW1eiupApTJBPWbwE3/QG3bgGPpHPRHr6PadkINGIUKDnV3iEIIIYQQQniM4rmgzpfn6VJuSJ4v0XfxBJsmkwm73c7hw4d55513mDt3LqGhodx9993nTBkpS8uWLWnZsiXjx4+ne/fuvPrqqzRr1owtW7bQu3fvi45fVC3plHAjffQAjtlfwumTqK59jYSNGWno9DTIcD5SU4ypCTnZuPxT9vYG/0CjXEHBn9uDQozOh/bdUBHR4HyogMBLjtkTRkeUhzKZoF0XTM3boVcvQa/5Gf3HFmN1kO4DUF5e7g5RuFnh3fgXX3zRzZEIIYQQojYy//PZopxO8fHx57ynfHzOO/JBBQVf9MiI8ykrz1O3bt34/vvv6dWrF3/88Qc7d+502TcgIICzZ8+WetzC/Xv37s2+fftITEykcePGbN26tcw4/Pz8CA4O5sSJEyxbtqxo1a/SZGZmsnnz5qJRmNu2bSuq0zvvvJNnnnmGjz/+mKioKHJzc/nss8+4+eaby1cpospJp4QbaLsdvWIhevUSCA7FdMPfjOSMTqV97dd5uZCRDmfTID0NXdhpkXUWgkOdnQ8xEBFljIAQgPEfuhowEn1FN/TiH9C/zEdvWoNp0Bho2b7GdLKIyvf5558D0ikhhBBCCPcpHIFcslPCXcrK83TjjTdy7733MnjwYFq1akWHDh1c9g0LC6NLly4MHDiQAQMGFK3CATBlyhQeeughBg0ahNls5pVXXsHnPEnpW7duTZs2bRgwYAD16tWjS5cu541ba81bb73Fgw8+iK+vL/7+/rzyyisADBo0iJMnT3LttdeitUYpxcSJEytWMaJKqfMNpalh9LFjxyr1gBEREZw8ebJSj6lTjqFnf4FOPoZq3xU19Koa1YlQFXVSnfShvegF36NTjqPqNTLqP+bSPgRqep1UFU+vl88++wyA66+/vsrOkZWVdc6QSHesvuHppE5cXWqdlPy7A4iNjYXS+7xF5asR7ZGaTurEldSJK0+rk9L+fy7slAgPD6+2OOSz15XUiavqbI/ISIlqoh0OdOFSmr5+mKx/QTVr4+6wah1Vvwncch9q0284fp6Hfn86qkM31ICRqIAgd4cnqlFVdkYIIYQQQpRHdXZGCOGppFOiGujTqegfvkAfOYBq3hY1ckKl5HcQF0eZTNCxJ6ZWHdDLF6LXr0Tv3ITqNQTVtrORYFQIIYQQQgghRJW79HUnRZm01uiNv+J49yV0yjFMY69Djb9JOiQ8hPL1xzT0Kky3P4BKaIReOgfHa/+m4I2nccz+Ar1xNfpEkrHMqLjsLFy4kIULF7o7jCqXkJDAkCFDGDhwILfddhvZ2dnVHsP999/P7t27AXj99dfPeW/UqFGVco7t27czZswYBg0axJQpU8jIyAAgLy+Pe+65h0GDBjF48GBWr15dKecTQgghKkNaWhppaWnuDkMIt5KcEudxKfPQdEY6eu5M9N6dqIZNUaOvRYXUqdT43MHT5uZVJn38iLHSyZGD6CP7ISvTeMPXDxXfABIaoRIaQGw9lOXP1Tsu5zq5FJ5eL3FxcQAkJiZW2Tk8IadE06ZN2bNnD2Bkn27bti2333570fsFBQVFa5NXdzxQeXUycuRIpk2bRo8ePfjqq684fPgwDzzwAB999BGbN2/mlVde4eTJk1x//fX89NNPmEye2ycvOSVqPI9qj1yupE5cSZ248rQ6Ke3/582bNwPQvn37aotD8ie4kjpxJTklaji9YxOOn74Gez5q2DhUp17GlAHh0VTdBFTdBOjuXEv51Ek4egB9ZD/6yEHYu9NYltVsRtWNNzop4hvi8OlYlMlX1ByDBw92dwjVrmvXruzcuZPVq1czffp0oqOj2b59O/Pnz+fhhx9my5YtmM1mHn/8cXr16sXMmTOZP38+eXl5HD58mHHjxnHvvfcC8M477zBz5kwAJk2axK233kpWVha33347x48fx+Fw8I9//IOxY8cyfvx4pk2bxty5c8nJyWHIkCE0b96cN954g4YNG7Jnzx601jz99NMsW7YMpRR33XUXY8eOLYq1Tp067Nq1i3bt2vHf//7X5d/bvn376N69OwB9+vRh8uTJPPDAA+zevbtoXfKIiAiCg4PZvHkzV1xxRTXWvBBCCFG64GCZNiyEdEpUIn0iyVhy8o8txhfcsdcZS3WKGkcpBeGREB6Jat8VAJ11Fo4eRB85AEcOoNcuR/+6jNPff4IDIDAYFRQCQcEQGGK8Dg6BoJA/X59n6SNRvT7++GN3h1Ct7HY7y5Yto3///gBs2rSJpUuXUq9ePd5++20AlixZwt69e5k0aRIrVqwoKrdkyRL8/PwYNWoUgwYNQimFzWZjzpw5aK0ZPXo0PXr04NChQ8TExPDpp58CkJ6efk4MjzzyCDNmzGDRokUu8f30009s376dRYsWcerUKUaOHFnUybBt2zaWLl1KTEwMY8eOZd26dXTt2vWc/Zs3b87ChQsZNmwYc+bMofBOdatWrViwYAFjx47l2LFjbN26lWPHjkmnhBBCCI/QsGFDd4cghNtJp0Ql0MnH0CsXoXduBi9vVL/hqF6DUKbqGxItqp7yD4RmbYpWTdH2fDh+FP+zp8k4lggZaZCRjk5KhIydkJ+Hy+Qob28ICjE6L8IiUVf0MEZdiFohOtropCw+ZWTKlCksXryYGTNmMHToUMBYrvTBBx9k8uTJvPjiiwAkJSXRqVMnoqOj2bhxY7nPWTgyAaBbt25MmjSJ9evX06FDB+rVqwfAunXrmDp1KgBNmjQhPj6e/fv3A8aog7CwMABGjBjB2rVrUUoxfPjwoiF5I0aMYM2aNfTv35+nnnqKZ555hsGDB9OtW7dyx7l27VquuuoqzGYzkZGRdO/enc2bNxMYGEiHDh0Kh/vRunVrjhw54tIpMX36dKZNm8Yrr7zC0KFD8fIyplhde+217NmzhxEjRhAfH0/nzp2xWOSjTwghRO2VkpLCo48+yubNm/H29iYhIYF///vfNG7cuNTyhVMvk5KSmDZtGu+99x4Af/vb39i9ezdWq5XbbrvtkmLavHkz33zzDU899dQlHUfUTNIyuwQ66Sh6xSL0rq3g7Y3qNRjVra/x5VVc9pTFCxIa4hfRhcxS5ivq3Fw4mwbpaXA2DZ2RbrzOSIeMM+it69Ebf0XFN0B17QPN26GqcW6/qB18fX1LHZlQfI7f+XILlZwmoZQqs3zjxo2ZN28eS5cu5bnnnqNfv37cc8895YrzfDF4e3sXPTebzaXOb2zSpAlffvklYEzlWLJkCWDMh3ziiSeKyl155ZVyV0oIIUStpbXmpptuYvz48bz11luAMSLx5MmTZXZKFIqJiSnqkEhJSWH9+vWsXbu23Oe22+1l3hho3759tebVEJ5FOiUugj522OiM2LMdfHxRfYeiuvRF+flfeGdRaygfH/CJgvAo43WJ93VOFnrzOvS6FejvPjVGUHTqaYyekBVaqlx1JLosKTk52eULdWnTSK6//nquv/76c7bFxMRUWazdunXj+++/p3fv3uzbt4/ExEQaN27M1q1bWbFiBadPn8bX15cFCxbw8ssvYzKZuOeee7jzzjvRWjN//nxef/11kpKSCA0N5ZprriEgIACbzeZyLi8vL/Lz84tGMhTq3r07n332GRMmTODMmTOsWbOGadOmsXfv3nJdw8mTJ4mIiMDhcPDaa69xww03AJCdnY3WGn9/f5YvX47FYqFZs2aXXmlCCCFEJajuRJerVq3Cy8uLG2+8sWhbmzZtyMzMxGq1kpaWht1u54EHHmDYsGHn7HvkyBGmTJnC0qVLue6660hNTWXIkCE8/fTTBAQE8NBDD5GTk0P9+vV5+eWXCQ0NZfz48XTq1In169czZMgQFi9ezBVXXMHq1atJS0vj5Zdfplu3bqxevZq3336bTz75hN9//53HH3+cnJwcfH19mT59Ok2aNKmW+hHuIZ0SFaCPHjA6I/b9YazI0H8EqnNvlK+fu0MTNZDy9Ud164fu0sdIorluBfrneeiVi1CtO6K69EbFyNQOUfWmTJnCQw89xKBBgzCbzbzyyiv4OPOfdOnShbvuuouDBw8ybty4okbThAkTipbznDRpEm3atOHnn3/m6aefRimFl5cXzz33nMu5Jk+ezODBg2nbti1vvPFG0fYRI0awYcMGhgwZglKKRx99lKioqHJ3SsyaNYuPPvoIMFbimDhxImB0Vlx33XWYTCZiYmJcliQVQgghapPCpNEl+fj48MEHHxAUFMSpU6cYM2YMQ4cOLTOR+4wZM5gyZUrRaMzBgwfz1FNP0aNHD1566SWmT5/Ok08+CRg5pr799lsAFi9ejN1uZ+7cuSxZsoTp06cXJc4u1KRJE7777jssFgvLly/nhRdeKBqhIS5P0ilRDvrwPvSKhegDe8A/ADVwFKpTb0laKCqFMpmgWWtUs9ZGstT1K9Fb1qE3r0UlNER16QMt2kqOkkpWnSMk3Kn48puFevbsSc+ePYte+/r68uqrr5a6f0REBM8884zL9ttvv/2cpUUB+vfvX5RIs7hvvvmm6Pmjjz7Ko48+WvT6wIED2O12lFJMmzaNadOmnTfW0mIBuOWWW7jllltctickJBQl7RRCCCE8xfvrkzlwOgcIBcC26NAlH7NhHV9u6XxxSfa11jz//POsWbMGpRRJSUmcOHGCqKioC+6bnp5OWloaPXr0AIwbF8XbCFdeeeU55UeOHAlAu3btOHr0aKnHu/vuuzlw4ABKKfLz8y/qmkTNIZ0SZdBak793J44fbehD+yAgEDV4DKpjT5S3dEaIqqEiY1AjxqMHjERvWmt0UHz3iTG1o3Mv1BXdJWeJEEIIIYS4KM2aNeOnn35y2f7dd9+RmprKvHnz8PLyolu3buTm5lbKOYvnsYI/c0WVlSfqpZdeomfPnnzwwQccOXKE8ePHV0ocwnNJp0QZ9KzPSN+1Fe3rhxoyFtWxB8rL+8I7ClEJlK8/qnt/dNe+sHeHMbVj2U/oFQtRHbqhBoyWkTqiyk2cOLFoGoQQQgghKs/Fjmi4VL179+aFF17g888/Z/LkyYCx/HdiYiIRERF4eXmxatWqUkcwlCU4OJiQkBDWrFlDt27d+Pbbb4uW9b4YGRkZxMTEAJSan0pcfqqlU8JqtX4IjAZSbDZbmxLv3Q+8BETabLaTzm0PAzcDBcBdNpttQXXEWZxq2oqAVu3IaNzKWGVBCDcwpnYYy5DqE0notcvR61eh9+3CdNVkVFx9d4dYY02ZMgUoPdGkEOLyVBPbI0KIy9uBAwcAqm1lKKUUH330EY8++ihvvvkmPj4+xMfHc9999zFt2jRGjBhB69atK5xY8tVXXy1KdFmvXj2mT59+0THecccd3H333bz77rv06tXroo8jag51vmXYKovVau0LnAU+Kd4IsFqtCcD7QAugk81mO2m1WlsBXwJdgVhgMdDMZrMVXOA0+tixY5Uad0REBCdLWeqxNpM6cVXddaIP7cMx+3PISEP1GYrqPdgj8014+t9Kday+kZmZSUBAQNFri8VS6jDF2kzqxNWl1knJvzuA2NhYcF0EqNaR9sjlQ+rEldSJK0+rk6ysLJepDNW9+gbIZ29ppE5cXWqdlPb3XlZ7xHTRZ6kAm822HDhVyluvAA8AxXtGxgJf2Wy2XJvNdgDYi9EgEEIAqn5jTLfdj2p1BXr5AvQnb6BPp7o7rBpnxowZzJgxo0rPYTKZ5ANOVCu73Y7JVC0f7TWStEeEEJ6mQYMGNGjQwN1hCOFWbsspYbVarwQSbTbbZqvVWvytOOC3Yq+POrcJIZyUrz9q3PXopi1xzPsW/d5LmIZdDe26lLl0kzjX0KFDq/wcvr6+5OTkkJubi1IKHx+fSksadbmQOnF1sXWitcZkMuHr61sFUV2+pD0ihHCnkJAQd4cghNu5pVPCarX6A48CpX0rKO0bValzTKxW623AbWAkQYmIiKi0GMEYslLZx6zppE5cubVO+g+joF0nMr96n/x53+CdeICAa6ZgCnD/Ch3yt+JKhga6kjpxJXVSfaQ9UnNJnbiSOnHlaXWSnJyMxeIZ6wx4ShyeROrE1aXUiY+PT7n//bmr5hsDDYHCuxLxwEar1doV405EQrGy8UCpkzNtNtu7wLvOl7qy54x52jw0TyB14soT6kSP/wv6t5/J+vknsvbsxHTldaiGzdwakyfUy/l89tlnAFx//fXVdk5PrxN3kDpxVRV14pzDKVxJe6SGkjpxJXXiytPqJDc3F7P53DxgqanGFNzw8PBqi0M6v11Jnbi61DrJzc11+fdXVnvELZ0SNpttKxBV+NpqtR4EOjsTS/0AfGG1WqdjJJZqCqx1R5xC1BTKZEL1HIhu2AzHrM9wfP42qmtf1MBRsnpMGR588EGgejslhBCeRdojQgh3K1x6szo7JYTwNNWSDctqtX4J/Ao0t1qtR61W681llbXZbNsBG7ADmA/8XzkyXQshAFU3HtMt96I69zKWD/3wVXRK5WaBv1xMnjy5aH1uIUTtIO0RIYSnCQ8Plw4JUetVy5Kg1USW4KoGUieuPLVO9N4dOH78CnJzUANGobr0QVVjVn5PrRd3kjpxJXXiqgqnb0gW3Ooh7ZFqIHXiSurElafVSWlLJFa3hIQEWrZsid1up0mTJrz22mv4+flVawz3338/t912G82aNeP111/nrrvuKnrvyiuv5Icffrjkc2zfvp2HHnqIrKws4uPjeeONNwgKCiIvL48HH3yQLVu2oJTiySefpGfPnjJ9oxSX3ZKgQojqp5q0wnTbP1ENm6MXzUbP+gztcLg7LCGEEEII4Sa+vr4sXbqUpUuX4u3tzSeffHLO+wUFVT8g7D//+Q/Nmhm5z/773/+e815ldEgA/POf/+SRRx5hyZIljBgxgrfeeguAL774AoAlS5bw1Vdf8eSTT+KQ9rHbSaeEEJcxFRCEsv4F1X8Eescm9C/z3R2Sx0hKSiIpKcndYQghhBCiFsvPzyc/P98t5+7atSsHDx5k9erVjB8/nv/7v/9j0KBB5OTkcM899zBo0CCGDh3KqlWrAJg5cyZTp05l8uTJ9OnTh+nTpxcd65133mHgwIEMHDiQ9957DzDulN9www0MHjyYgQMHMnv2bADGjx/P5s2befbZZ8nJyWHIkCHceeedADRt2hQwlrl+6qmnGDhwIIMGDSratzDWW2+9lb59+3LnnXdS2sj/ffv20b17dwD69OnDTz/9BMDu3bvp3bs3YIykCQ4OZvPmzZVet6JiZN0TIS5zSinoNRjOnEKvWowOj0S16+LusNyuU6dOACQmJro5EiGEEELUVjt27ACgffv21Xpeu93OsmXL6N+/PwCbNm1i6dKl1KtXj7fffhswRhPs3buXSZMmsWLFiqJyS5Yswc/Pj1GjRjFo0CCUUthsNubMmYPWmtGjR9OjRw8OHTpETEwMn376KQDp6ennxPDII48wY8YMFi1a5BLfTz/9xPbt21m0aBGnTp1i5MiRRZ0M27ZtY+nSpcTExDB27FjWrVtH165dz9m/efPmLFy4kGHDhjFnzhwKp9W1atWKBQsWMHbsWI4dO8bWrVup7Cl3ouJkpIQQtYBSCjViPKpBExxzZqIP73N3SG4XHR1NdHS0u8MQQgghRC0VFxfHyJEj8fL6c6W0KVOmEBcXx8KFC4u2ffbZZ8TFxfHAAw8UbUtKSiIuLo6OHTtW6Jw5OTkMHDiQESNGEBcXx6RJkwDo0KED9erVA2DdunVcc801ADRp0oT4+Hj2798PGKMOwsLC8PPzY8SIEaxdu5a1a9cyfPhw/P39CQgIYMSIEaxZs4YWLVqwYsUKnnnmGdasWUNwcHC541y7di1XXXUVZrOZyMhIunfvXjSioUOHDsTGxmIymWjdujVHjhxx2X/69Ol89NFHDB8+nMzMzKI6vvbaa6lbty4jRozg8ccfp3Pnzlgscp/e3eQ3IEQtocxmuOYm9IxXcXw9A9PUu1FhEe4Oy202btzo7hCEEEIIIWjVqlW1naswp0TJBIbFExKebyEEpZTL67LKN27cmHnz5rF06VKee+45+vXrxz333FOuOM8Xg7e3d9Fzs9lcajLGJk2a8OWXXwLGVI4lS5YARvLGJ554oqjclVdeScOGDcsVk6g6MlJCiFpE+fljmngraI1j5vvonCx3hySEEEIIUSslJia6TCP9+OOPSUxMZOjQoUXbrr/+ehITE3nxxReLtsXExJCYmFglN1m6devG999/Dxhf6BMTE2ncuDEAK1as4PTp02RnZ7NgwQK6dOlC9+7dWbBgAdnZ2WRlZTF//ny6detGUlISfn5+XHPNNfz1r39l69atLufy8vIqNadG9+7d+eGHHygoKCA1NZU1a9bQoUOHcl9D4aorDoeD1157jRtuuAGgKEaA5cuXY7FYipJuCveRkRJC1DIqPBLThL/g+Pwt9Lcfw7W3GaMohBBCCCFErTdlyhQeeughBg0ahNls5pVXXsHHxweALl26cNddd3Hw4EHGjRtXlAtjwoQJjBo1CoBJkybRpk0bfv75Z55++mmUUnh5efHcc8+5nGvy5MkMHjyYtm3b8sYbbxRtHzFiBBs2bGDIkCEopXj00UeJiopi79695bqGWbNm8dFHHwEwcuRIJk6cCBidFddddx0mk4mYmBhef/31i64nUXnU+YbG1DCyLng1kDpxVVPrRG9ei+PHr1BXdEeNnOAyHO9SeXq9DB8+HID586tvRRJPrxN3kDpxVRV1Uta64KJKSHukGkiduJI6ceVpdZKVlXXONAkwVoMAqvVuvcViKXXKw4XMnDmTLVu28Mwzz1RBVO51sXVyObvUOint772s9oiMlBCillLtu6JSU9Crl0JENKpbP3eHVK1KG0IohBBCCFGdsrOz3R2CEG4nnRJC1GKq/0g4dQK9aDa6TgSqWWt3h1Rt5s2b5+4QhBBCCFHLNW3a1N0hlNvEiROLpkEIUZkk0aUQtZgymVBXXoeqG49j1qfo5NqzTnO7du1o166du8MQQgghRC3m7+/vMsRdiNpGOiWEqOWUtw/KejP4+BkrcpxNd3dIQgghhBCXncsol58QF1SRv3fplBBCoIJCME28GbIz0bYP0KUszXS5efnll3n55ZfdHYYQQgghagmTyeSSODApKYmkpCQ3RSRE1bDb7ZhM5e9qkJwSQggAVEw8pqtuwPH1h/DDFzDuBlQF/jOpaaZPnw7Afffd5+ZIhBBCCFEb+Pr6kpOTQ25ubtGqZ4sWLQLgmmuuqbY4fHx8yM3Nrbbz1QRSJ64utk601phMJnx9fcu9j3RKCCGKqOZtUIPGoJf8COGRqP4j3R1Slbn33nvdHYIQQohqpBMPQ34u1GuEMpndHY6ohZRS+Pn5nbMtJSUFoFrzSnjaUqmeQOrEVXXWiXRKCCHOobr3h9Rk9MrF6PAoVNvO7g6pSsgICSGEqB10ViZ68Wz0lvXGBv8AVPO2qJbtoUET6aAQbiXtESGkU0IIUYJSCoaPh9OncMyZicrMQDVtgwqPdHdoQgghRLlprWHHJhwLvoOcbFSvwaiYOPTOzehtG9G//wZ+/sU6KJqizNJBIYQQ1U06JYQQLpTFAuNvgi/fRS/+Eb34R6gTjmrSEtW4JdRvgvLycneYl2TLli0AsiyoEEJchnTaafS8b9B7d6JiE1CjJ6L370ann0ENHoMaMwn27TQ6KHb8jt60Bnz9UC3aolp2kA4KUW2kPSKEdEqIKpJrd5B8Np+ks3kkn83HpBQtI/2oH+qD2aTcHZ4oB+Xnj/rL3ejTqei9O43G2++/odetBIsFVb8JODspVFiEu8OtsBEjRgCQmJjo5kiEEEJUFu1woDesRi/8DrIyyUpoRsrY20nJLODYhiMEJB+i5RefEBcWgKlle1SHbqjR18L+P5wdFJvQm9YaHRTFR1BYpMksqoa0R4SQTglxkbTWnMkpICkjj6Sz+UUdEEln80k6m8/pbHup+/l7mWgR4UerKD9aRfnTNNwXb/Plu8LD5UDVCUd16Q1dehtLhR7eh967E71vJyz4Hs33EBZRbBRFY5TF80dRtG3b1t0hCCGEuEQFDs2pbDtJZ/M4vmkryTt3kaR9SQ4bTnJsGOnegTD/sFE4tIvxAIIdObQ8vo9WuftpHd6Yho1bYdq/CzV2MjgcsHsb+o/N6M3ODopGzVD1m0KDJhAWWbRyghCXStojQkinhADsDk1WXgGZ+Q6y8h1kFj7PKzBeO59n5jvIdqRw5NRZks7mk1egi46hgHB/CzGBXnSKDSA60IuYQG+iA72oG+hFboFme0oWO1Ky2XEii882ZwJgMSmahvvSKtLopGgZ6UeAtwyX9FTKywsat0A1bgGMQ586gd73B+zdid74K3rtCmMURcNm5A0ciY6o67ENt/nz57s7BCGEEMXkFxS2ORxk5heQmecgK9/ZFnE+z8wzymTmHOHIiTRS8kzYVeHNjVBMEZ2JLMgkxt9Mj/BgoqPqEBPsXdQmScspYEdKFjtOZLE9OZA1mXaYfwhfMzQ76UOrDctomZ1Is7qh+LbvBuGRkHgIfWAPesdm4zRBwaj6TVANmhijKELD3VZnouaT9ogQ0ilRq2it2Z2aw4qD6WxOyiQjz+hsyC3WuVAWX4siwMtMnQBv6gZ5c0XdAKIDvYkJ9CImyJuoAAteFxjx0L9hCP0bhgCQnlvAzhPOToqULGbtPMW3O06hgAZ1fIo6KWKDvPG1mPD1MuFrUfiYTTL9w4OosEhUWCR06YPOz4NDe41RFLu2kfHBq+jwaOg9GJq3RZlkRIwQQghjdMOWpExW/pHEzpPZZCpvsgo452ZHWfwLcvC35xCcd5YG2al0y08nOsCLmOyTRNePJ3LoCLxCQsrcP9DbTFywN0OahAKQmpVfdMNkR0BLZtZpgkZhcRTQaO9RWukgWnccQ3j8QXxXL8SnIB/f3Ax8NqzCtOYX8POHOhEQ3xDVoAmqcXNUcJ3KqiohhKgVpFPiMqe15sDpXFYcSmfloXRSMu1YTIp20f40i7AQ6G3G38uEv5eJAG8zAV4m/L1NBHiZCfA24e9lvF/YEVBZ69UG+5jpFh9Et/ggAHLsDnafzGZHSjbbT2SxeF8ac3efKXVfb7MyOiosxk8fi8n52tjm52Wid/1g2scEXHKcovyUlzc0aYVq0go95CoCDu0mbd736G8/RkVEQ6/B0LqDLL0mhBC1kENrdiRnsmLbUVYn55OOF372HNqe3kto/XoENG6Af3Yafst+IEDZ8VcafzMEWDSBg0YQ0KotvqnHMS1eCkG+BMTEcjYxDfbsBN86mK4aj2rSqsJxhft70aeBF30aBANwNreAP05mG6M7j3kzJ7WAWb8kAl5QZ9Q5+/oU5OFjMeGn7ficOovv8TR8V63E12HHx6QJsCiGDriCxs0bVEINCiHE5Us6JS5Th9NyWXEwnZWHMjiWkYdZQYe6AUxqF0m3+ECPmyLhazHRLiaAds6OBLtDc+B0Diez7OTaHWTnO8gtcJCTr8mxO4o9jNe5dgcns/LJtTtIyy1g4d40+jcI5i+dogjxlT/z6qbMZny79iGjQXPYsQm9chGO2Z/D8vmYeg2CNp3dnjSsY8eOAGzcuNGtcQghxOVKa83ulLOs2HOS1SkFpGbb8S7Io/OpP+jtdYaOLeLw6d0GAoJQZgfYfSFhHBTYIT/f+GnPh/xs9ObVxrYGTSA/j7xtv0PqCVTXvqj+I1E+PpUSc6CPmc5xgXSOC4Qrosi1O9h3Koe0nAKyne2N7LwCcrKzycm2kOvtT04BZKf5knvaTHa+nfQCyMHMGa8AFq7LYszhbVzbIhi/unVrRM4lUb2kPSKEdEpcVo5n5DlHRGRw6EwuJgVtovwZ1yqM7glBBPt4VkfE+Ri5JvxoehHTNPMKHHy9LZXvdqSy4dhZplwRxaDGIZg8NLfB5UyZTNCmI7TqgNqz3eicmGOD5QtRPQairujmtgZacnKyW84rhBCeSmsNR/aDyQSBIRAYXOEOZK01+5PSWPH7flalQorJH4suoGOUDzdF5dExcQN+de2oM2fQa/egf11i7FeRkyiFKaEBpjGTUPENKhRfRflYTLSK8i9HydhzXmmHg7N7dvPJrweZndKAVUeOcduRj+nSuwOq99BK60QRNZ+0R4SQTokaTWtNSmY+vx7JYMXBDPaeygGgZaQft3WOpme9IOr41b5fsbfZxOT2kfRtEMz/1iTxxpoklu5P42/dYkgIkUaAOyiTCZq3hWZtUPv+QK9ciF7wHXrlIlSP/qgrelZ7A23Dhg3Vej4hhPBkOv0Meq4NvXMznE0HZQKTAh9f8AuAhIao8Ci0xYIqKICQOhAaBnUicQSFcMxuYeWBdFbuSCRRBWDSPrRP24f17F66ZR4k4GCx9khoGEREoxq3QNUJBy9vsFjA4gVmC3h5Gc8tFjB7gVfhe8ZzZTITUknTSauKMpkIat6CvzVtSv+Va3lrr+bZJhPosWULNy/4B+H9B6H6j0T5y1TT2k7aI0JIp4THyy9wkJyZT3JGvnO5zTzn8pv5JJ/NI8du3FtoEubL1I6R9KoXTGSADA0ESAjx4Zkh9Vi6P40ZG1O4+6cDXN0qnAltwmUZUjdRSkGTlsYKHof3oVcuQi/+Eb1qCapbP1Tn3ihfv2qJJSYmplrOI4QQnk5v/x3HT18b0yXadITlCyA/z1ga0ynXL5ik0zkkn80n2W4myTedZL8Mkn0zSPELI9/khdKa1pmpjEn9he75RwmOikI1iYGIlkZuoYhoCI8yVnKqJZTJTOu+PZjeKZPZCzdgi2jFpvDmXL/mJ4ZFb8GrUw93hyjcTNojQkinhEfQWnPoTC6H0/L+7HTIyCPpbD6pWfZzhjT6mJWxrFWQF+1i/Kkb6E3H2ADqBnm7LX5PZlKKwY1D6RwXyIyNKdi2pbLiUDp3dI2RRJhupJSC+k1Q9Zugjx5Ar1yM/nkeetViVN0EiI5FRcdBTJxxN03m4AohRKXT2Vk45syEX5eBxYz+5/PsV8Ec7Tye5LN5HE/PIzkjl+RMO6dznR0UEcYPP1VADDkkFJylS94B6gZZ6BzlQ3hMDER2gNBwlLnmTButat4BAUwY15dehxJ5e+Uh3ms2jp+3neIOy04aHt0KZ9NRQ8cZI0eEEKKWkU6JMhxOy8XunVOlFXQq287PB9JYtj+Nw2l5Rdvr+FmoG+hF22j/onW1Y4K8iAn0JtTXbHyhExUS6mvhnp6xDGgYwtvrkvjXkiP0bxjMXzpKIkx3U/ENUdfeik46it60BpKOojetNZYYBTCZUJExEB0L0XGomHiIrovyLc8c37I98MADALz44ouXeglCCFFl9qbmYPbPu3DBCtJ7d+L49iM4vJ/j5kCWdbTyy8oMTmSdBkABEf4WYoK86RTvR0ygF9GB3tQNMn4GeZukPXIRYuvH8e+EuqxY8wcf7PXn/q2aMWcKmLh9Ab7LfkL1HIgafjUqKvbCBxOXBWmPCAFK6wqlFvJk+tixY5V2sKd/Psq6xLPUD/WhS1wgXeICaRruW7Q05sXKK3Cw5shZlh1I4/fjmTg0NI/wY2CjYFpF+RMd4IWPxXOnFlTWkqDuVDwRpp/FxE0doxjUKOSiG1eXQ51UhUupF+1wwOlUSD6KTkqE5GPGz8yMPwuFhhmjKaJjjbXh6zWu0Dni4uIASExMvKgYL4b8rbiSOnFVFXUSGxsLxvdMUfUqtT3yfz/uJzE9j2YRvkXtkfqhPhf9maVzc3Esnk3mymWs9o5nad0u7Aqqh0lB+5gA+jcMplm4H5EBXniZPfdP5nL4vyMjK5dPlmxjYXoAEfkZ3HbmVzrv+gUKClDjp2AaOq5Cx7sc6qSy1YQ6kfaIZ5A6cVWd7RG5RVyGqR2j6NYogp93p/DdjlS+2Z5KsI+ZTrEBdIkLpEPdgHIvq6m15o+T2Szbn87KQ+lk5juI8LdwTatwBjQKIS5Ypl5Up5KJMP/7m5EI846ukgjTUyiTCcIjITwS1eqKou36bDokJaKTj/3ZYbFrK3r5AtSAUcYdpnI21F944YWqCl8IISrNvb1i2X7awfI9KXy2+SSfbT5JhL+FLs5lK9tG+5f7Zob98H5+n7eUn3U0a1v9jTyTF/GBZm5sEkb/hsGE+8tUueoU5O/D/43pxIDDJ/nfqhyejRxKj8Bm3Jy/k/DY+gDoxEPo9atQfYagwiLdHLGoCtIeEUJGSpxXYe/Q2dwCNh7PZH3iWTYcO8vZPAdmBa2j/YvuWpSW0+FEZj7LnNMzjmXk42NW9KgXxMBGIbSN9q+RS1Rebr2IDq2LEmHm2B20ivSnbbQ/baL9aRruV667RJdbnVSW6qoXnZuL/smG3v67kSxz0BijU8MDyd+KK6kTVzJSosarsvbIqWw7GxLPsi7xLJuTMsmxa3zMivZ1jRsmnWIDSu1YOJyaydIVW/jljBenvAIJtECfuj4MbBlN0wi/GjkN43L7vyO/QDNr3QFse7NR2kGbnGO04Qxtco/TYPMyzApo0wlTvxHQtiPK5Hpj7HKrk8ogdVI6qRdXUieuPH6khNVqbQQU2Gy2Q+Us/yEwGkix2WxtnNteAsYAecA+YKrNZjvjfO9h4GagALjLZrMtuJg4K0ugj5m+DYLp2yCYAodm18ls1jkbBR9sSOGDDSnEBXsXNQhSs+ws3Z/G1uQsNNAmyo9rWofTs14Q/l6S9MmTFE+EOWvHKTYlZfLFlpNojKSiLSP9aBsdQJtof5qE+2K5xOk7ovIpHx8YOxn8A9BrfoGsTBg9URKsCSFc1PT2SJifhSFNQhnSJJS8AgfbkrNYl3iW9YlnWXv0LACNw3zoHBfIFTEB7D+dy9LdJ9ib7sCkQ+iY9ge31A+k65jBeMkqVB7Fy6yY0L0RvVrlMnvDEbaeiGNDfn3wb09An/60yjhMm9TdtJnxPvW9cjH/36OouAa1aiUTIcTlq1ydElar9UvgvzabbbXVap0K/A9wWK3Wu2w22wflOMRHwBvAJ8W2LQIettlsdqvV+gLwMPCg1WptBVwLtAZigcVWq7WZzWYrKPdVVSGzSdEqyp9WUf5MuSKKpIw81h87y7rETObsOs2snacAiAn04tq2EQxoFEx0oEzP8HShvhZu6hgFQEZuAdtSstiWnMXW5Cw+3XwCAF+LiVaRfrSN9qdtjD+N6lx6jhFROZTJBEPHgX8g+pf5kJMFV9+I8ir7397ChQsBGDp0aHWFKYRwv4+4TNoj3mYTHWMD6RgbyG2dNYfT8oo6KL7elsrMrakANMg9ydTj6+mTuJY63XuiRtyCkg4JjxUb7MMdA5oAkJqVz9bkLLYdPsW2lCasC20GjSHQnk3rhXtpk7OCNun7qVcvBlOXPjjaXoF2ODx2tKAonbRHhCj/SIlBwBTn83uBwcAZYBZwwU4Jm8223Gq1NiixbWGxl78B453PxwJf2Wy2XOCA1WrdC3QFfi1nrNUqJsib0c3DGN08jKz8ArYmZxHkbaZlZM0cDikgyMdMj4QgeiQEAZCWY2dbShZbk7LYlpLFx5uMTgp/L6OTonvjHOr7axqFyUgKd1JKofoMRfsH4Jj3LXzxDky8ucxVOqZOnQpUb2IpIYR7Xa7tEaUU9UN9qB/qwzWtwkjfs5stqzdS9/A2Gh7bCd7emG7+O6pTL3eHKiog3N+L/g1D6N8wBDCmBW9LzmJrYjpbk3xZk9cQwnsSnHeW1gt203b2z7TMPkZCoBlzeCTaPwC8/SA8ElNIHQgK+fPhK+1UTyHtESHK3ynhbbPZ8qxWaxwQZrPZVgFYrdboSorjL8BM5/M4jEZBoaPObR7P38tMt/ggd4chKlmIr4Ve9YLpVS8YgDPZdrY6R1FsTc7ijRUHAWO6R/NIP1pH+tMqyo/mEX4evZLK5Up16oXJzx/HrM/hkzdh0u2ooGCXcoMHD3ZDdEIID1dj2yNaa9i/C8fy+QQePkDP0DBUrx7ozWZMN/1dkiReBiIDvBjQKIQBjYxOiuSzeWw9nsHWndlsMzXkV0tbAAJ1Hi1UOi1TD9Bq/xoaZSTihQazGUwmCI8C/wAwWyA3B+Lqo0aMxxTjsX/elzVpjwhR/k6JTc55lfWBuQDODor0Sw3AarU+CtiBz52bSuu2LTUbp9VqvQ24DcBmsxEREXGp4ZzDYrFU+jFrOqkTiACaJEDhQl1ncgrYeOQ0mxPT2Hwsna+2GjkpzCZF86hA2scG0z42mHaxwYT41Z65n279W+k7hPyYODI++i/qy7cJvu0+zBHn9qHOnTu32sOSfz+upE5cSZ24R01tj2ityduxiazvPyd/707IOotX45bU+dd0I9/AxJsqNRZPIf9OICICWjeAa3s0R2tNSkYuG/YcY8vJPDYnh7CeCOjYBW8cNCedVrlJtEo7SLtOrQn0NpO7aQ15u7fBob3odSswNW9LwPgpeLfteNlMAakJfyfSHvEMUieuqrNOytspcTPwFJAP/NO5rQd/fnBfFKvVOgUj4dQgm81W+EF/FEgoViweKDWNtc1mexd41/lSV3Z2UMnC6krqxFVERATt6kC7OiHc0CaEzLwC/jiRzY4T2exIyeLrTcf4cqMxJC8hxJtWzpEUbaP9L+vl19z+txIWhb72VhxfvsvJV57ANOl2lJvvArm9TjyQ1ImrKsx2LcpQE9sj2uGAPdtxfPMRJB6E/HzjLnjbzhT0HkxqWlqlxuBp5P8OV9EREXStH0zX+gARnMmxszMlm+0nstiR4s/Xp0Nx+LTAdBQa1vGlVefWtBpyG60P/07gvM/J37aBM39sgVYdUJ17ozp0Q/mVPgWyppC/k9JJvbiSOnFVne2RcnVK2Gy2fcB1JbZ9A3xzsQFZrdbhwINAP5vNllXsrR+AL6xW63SMxFJNgbUXex4hqluAt5lOcYF0igsEIK/AwZ7UHHakZLEjJZvlB9NZsPcMJgV96gdzTetw6of6uDnqy5OKrYdpyt9xfPEOjk/fwGS9BVW/sbvDEkJ4kJrWHnGkn4F536LPpMKJJMjOgsgY1IDRqC69UQEyjVQYQn0t9KgXRI96xt9EVn4Bu04WtkeyWLD3DD8WaLzNCQye8BxjOULUno1gz0cv/gH945fQpQ+m3kNQkTFuvhohxOVMaV3qSMRzWK1WBdyCkYU60maztbNarX2BGJvNZivH/l8C/TFGvicDj2Nkt/YBUp3FfrPZbH91ln8UY16nHbjbZrPNK8e1VNm64OJPUieuKlonBQ7NoTO5/HwgjQV7z5Bj13SLD+Sa1uE0j/Crwkirlyf9rej00zi+eAfOnMI07gZU87bExRmjJqozsZQn1YmnkDpxVZ3rgtc2NbU9Eh4UxIklc9GLf4ADewANTVtjGjEe3bIdJsvlO+quLPJ/h6uK1kl+gYO9qTks3p/Gsv3GyJp+DUO4plUYsQc2od98BpQJgoKh9RWYeg+BJq1q1NSOmvB3Iu0RzyB14qo62yPl7ZR4ChgCvAq8bbPZQq1WayPga5vN1qlSI7140ilRDaROXF1KnaTnFjB31ynm7DrN2TwH7aL9Gd8mnHbR/jU+K7an/a3orEz0V++ijx/FNMpK/KirAWkEuJvUiSvplKjxKrU94jh2CP3M/ZCXayQqjIhGDboS+g7DZDZX2nlqGvm/w9Wl1MmJzHxm7TzFwr1nyC/Q9KgXxDURuTT85WvYuNooFBAE9RujuvdHta8ZUztqwt+JdEp4BqkTV9XZHilvTombgCtsNttJq9X6lnPbAaBRpUQnRC0V7GNmUrtIxrYMY8GeM8zeeYp/LTlC03BfJrQOp0t8IKYa3jnhKZR/AEz+G3w7A8ecmRz5+lNMPQe6OywhhCiTdjjQc2woP390w2aoEeNRLdvXqDvVomaIDPDi1s7RTGgTzo9/nOan3adZfdhBx2bXcc3AybRc+wNs/BVCwtCLf0QvnYvqMQDVZyiqFo7UqUyyFKgQ5e+UMANnnc8Lh1YEFtsmhLgE/l5mxrUKZ1TzOizdn8Z3O07x7PJE6oV4c03rcPrUD8Zsks6JS6V8fMB6C/zwBXrpHBzpp4152D6S00MI4XmUyYSpYw9CrryWtKg46YwQVS7U18INHSK5ulUY83af4Yc/TvHo8QJaRY3imnuuo1N8CPrYEfSLD6FnfY7etQ3TNVMk54QQ4pKU99PtJ2C61Wr1gaIcE08BP1ZVYELURt5mE8Ob1uGtMY24p2ddAF5ZfZw7ftzP/D2nyStwuDnCmk9ZLKirrkd17YtevwrHW8+it643MtkLIYSHUZ17493mCumQENUqwNvM+DbhvHdVY27tHEVKZj5PLU/innkHWZ0bhGPgGMjOhG0bcLz9PI51KynPlHAhhChNeT/h7sXIPJ0GhGCMkKiPka1aCFHJzCZF/4YhvDaqIY/0jSPYx8xba5O5bfZ+5u46jUM++C+JMpmY+vn3/GXrUVRQKI7ZX6A//i868bC7QxNCCCE8ho/FxOjmYbx9ZWP+3j2GXLvmpV+TuUt149cbnzSWoT1+FD37c/RX76Ez0t0dco0zZcoUpkyZ4u4whHCr8i4Jmg5cZbVao4F6wBGbzZZUpZEJITApRbeEILrGB7IlOQvb1pO8uz6Z1YfTuatHXaIDvd0dYo21ePFiANSXX6G2rsexdA56xquodl1QA0ehAoPdHKEQQgjhGbzMisGNQxnQMITfjmRg25bKS4d86DXmaW5b+SZBOg99cA/6vZcwjZqIat7G3SHXGIXtESFqszI7JaxWa2mjKE44H0Xv22w2GfMsRBVTStE+JoB20f4s2Z/G++tTuGvuQW7pFMXgxiE1fqUOd5gxYwZgjJqgfVdMLdqhVy5Cr/kF/cdmVO+hqK59UZbypt4RQgghLm9mk6JX/WC6JwTx3Y5Uvtp6kh1t7+D/2gfTuY7C8c1HOGwfoK7ojhpyleRsKofC9ogQtdn5Wtt2/kxqWRrlfL/2rkclRDVTyrhT0S46gNd+O84ba5L47UgG/9e9LmF+8uW5IoYOHXrOa+Xjixo0Bt2hO3rxbPTSOehNv2EaPBaatpKOHyGEEMLJbFJMaBNBp9hAXl19nKfXnmFI4xBuys7FLycLvWE1+tA+TFdNRsXVd3e4Hq1ke0SI2uh8OSUaYiz5Wdaj8H0hRDWLCvTiqUEJ3NIpii3JWdw1Zz8rD8k8zsqgwiMxTbwF07W3glI4bB8Y82RPJrs7NCGEEMKjNArz5eUR9bm6VRhL9qdxT+zVbC8IgowzcDYdx0ev41i+AO0ocHeoQggPVuatVZvNdqg6AxFCVIxJKca0COOKugG8+utxXlp5jN+OZHB7lxiCfGQA04V89tlnAFx//fWlvq+atMTUsBl6/Ur08vnod19CdeljrMnu61edoQohhBAey8tsYsoVUXSNC+TVX4/zr7a3MDplLZP3zcO7dXv08gWw/w8Yez2qTri7w/U4F2qPCFEbqPIu32O1Wq8E+gERGFM3ALDZbDdWTWgVpo8dO1apB4yIiODkyZOVesyaTurElSfUSYFD8+12Y25nsK+Fv3eLoVNcoFtj8oR6OZ+4uDgAEhMTL1hWZ2agl/2E3rQG/AMwDb0K1aZThc/p6XXiDlInrqqiTmJjY6HYZ7eoUtIeqQZSJ648oU6y8x18/HsK8/acIT7/NHcd/JGmY69EL/4BtAPTsKuhXZdqmxLpCXVyIRVpj1SWmlAv1U3qxFV1tkfKtSSo1Wp9HHjHWX4CkAoMA85UWoRCiItmNimsbSN4aXgDgrxNPPnzUf63JonsfMlDW5bJkyczefLkcpVVAUGYRk/EdPO9qLAIHLM+xzHXhs7Pr+IohRBCiJrDz8vEX7vG8O+BCWQHR/BQsxuZaW5MwdR7IDIGx49fGR0UokhF2iNCXK7K1SkB/AUYYrPZ7gHynD/HAA2qKjAhRMU1DvPl5RENGNcyjIV7z/CPnw6wPSXL3WF5pBdffJEXX3yxQvuouvGoG+9E9RqE/v039EevoU+dqKIIhRBCiJrpiroBvD6qEX3rB/PV1lQeWniII8dPQct2xipXG1a7O0SPcTHtESEuN+XtlAi12WzbnM/zrFarl81mW4sxnUMI4UG8zSZu6hjFM0PqoYBHFx1mxsYU8gpk1ERlUCYzpgGjME28BZ12GscH09E7N7s7LCGEEMKjBPqYuadXLA/2iSXFFMD9CVZ+3J2OIzIax/xv0ft3uTtEIYSHKG+nxD6r1dra+XwbcIfVar0BOF01YQkhLlXrKH9eHdmQoU1CmbXzFA8vPMyZHLu7w/IYSUlJJCUlXfT+qmkrTLfchwqPwvHtxzgWzkLbpX6FEEKI4nrWC+aNsU25IsqXGfWG8kJ+c3ID6+D49mP0iYv/HL5cXGp7RIjLQXk7JR4DCtPlPgzcBbwE3FsVQQkhKoefl4m/dYvh4b5xHE7L5cEFhziekefusDxCp06d6NSp4skqi1OhYagb/47q0ge9djn60zfR6dJXK4QQQhQX6mfhkaGNuaWFH+tDm/Nv/55kWPxwzHwfnXnW3eG5VWW0R4So6cpcErQ4m832U7Hna4AmVRaREKLSdU8I4qlB9Xj6l6M8uOAQ0wbE0zS8di9rGR0dXSnHURYLatg4dEJDHHO+Qr/3Mqaxk1FNWlbK8YUQQojLgVKKMZ3qE84epu+qyyPhVqbtm0n0NzNg8l9RFi93h+gWldUeEaImO+9ICavVGmC1WgOKvVZWq/VWq9X6mtVqvbbqwxNCVJYWkX48P7QePhYTjy0+zIbE2n1nYuPGjWzcuLHSjqdadcB0832ooBAcX72HY9lPaIfk8RBCCCGK69mpKU8Ors+ZAsXDcePYn5SGnmtDa+3u0NyistsjQtREF5q+8RVwdbHX/wGeB2KB161W631VFZgQovLFB/vwwrD61A3y5ulfjrJk3xl3h3RZUeGRqKl3ozp0Ra9ajP78bfTZdHeHJYQQQniUVlH+PN/BG3N2Jo/FXsnm3cfRqxa7OywhhJtcqFOiM/AjgNVq9QZuBcbbbLYJwGjnayFEDRLmZ+HZIfVoG+3P678lYdt2stbenagKyssL0+hrMY2ZhE48iOO9l9GH9rk7LCGEEMKj1GvagOciE4nMSuXpuiP4ed0e9I5N7g5LCOEGF+qU8LfZbGeczzsDdpvNtgzAuSRo3SqMTQhRRfy9zEzrn0C/BsF8vvkk76xLpsBRuzomhg8fzvDhw6vs+Kp9F0x/uRt8fXF89j8cqxbLdA4hhBCimMgxV/Os3y5anDnIa1ED+W7ZFhyJh9wdVrWq6vaIEDXBhToljlmt1nbO50OBFYVvWK3WUCC3iuISQlQxL7Pi7p51GdcyjHl7zvDiykRy7bXnS/PWrVvZunVrlZ5DRcVi+su9qJbt0ct+IuP9V9AZMp1DCCGEACP5ZeDkW5nm+J2eKZv5JLQrH/z0OwVnTrk7tGpTHe0RITzdhVbf+A+w0Gq1rgaGcW5+iWHAlqoKTAhR9UxKcVPHKML9LXywIYXHlx7h0X7xBPmY3R1alZs3b161nEf5+MC4G1ANmmBfNhfHey9hGj0R1axNtZxfCCGE8GTKbMbntvu4b9NawvK9mXO4Badn/84/JvTEx//yXymsutojQniy846UsNlsHwATgVXAMJvNtqDY29nAE1UYmxCimoxpEcb9vWPZk5rDQwsPcSIz390hVbl27drRrl27CxesBEopVMeehNzzb1RQKA7bhzh++hqdn1ct5xdCCCE8mfL1w9K9H7f0bsiUOmdYZYnjie+3kJEj7REhaoMLjZTAZrP9AvxSyvYfqiQiIYRb9K4fTKivhWd/OcoDCw7x+IB4GtTxdXdYlxVzVF3U1H/AL/PQvy5DH9qL6aobUHXj3R2aEEII4X4FBYxd/j51ApvwRoMxPDJrB/++shXh/l7ujkwIUYUulFNCCFGLtIn259kh9VDAw4sOsyUp090hVZmXX36Zl19+udrPqywWTIPGYJp8B+Tl4ZjxKo7VSyUJphBCiFpPWSyYbr2fvsfW89j+rzmRr3jgx90cPnP5prFzV3tECE8inRJCiHM0qOPLC8PqE+5v4YllR3l/QzJbkjKxX2arc0yfPp3p06e77fyqYVNMt96PatYavXQO+ou30eln3BaPEEII4QlU4xaYbr2fdkd/5+n9X2HPyeWh+Qf4fPMJdp/MxnGZLWPu7vaIEJ7ggtM3hBC1T2SAF88Pqc8ba5KYt/sMP/5xmgAvE1fEBtAlLpCOsYEE1/BkmPfee6+7Q0D5B8A1N6E2r8Wx4Hv0uy9hGmVFtWzv7tCEEEIIt1FXdEdNuo2GX7zD895m3ogeyDfbNLZtqYT6mukcF0jnuEA6xATg51Wz77F6QntECHerUKeE1WpNAOJsNttvVRSPEMJDBPqYeahvHNn5DjYnZbIu8SzrE8+y8lAGJgXNI/zoEhdIl7hAEkK8UUq5O+QKue+++9wdAmAkwaRDN0wJjdCzPsPx7ceo9l1RQ8cZK3cIIYQQtZBpwCh0SB2i6zfmqW8/IT3pFBv9Elgf2pTVubEs3peGxQRtogPoGhdI57gAogO93R12hXlKe0QIdypXp4TVaq0HfAl0ADQQaLVaxwPDbTbbLVUXnhDC3fy8THRPCKJ7QhAOrdl3Koe1R40Oik82neCTTSeICvCiS1wAneMCaRvtj5e5Zt+1cAcVHgk33QXLF6BXLUYf3o/pqutRcfXcHZoQQgjhFqpjTxSg+40g6H/P0E8dpt/BFdgLHPwRXJ91sR3Z4GjGu8czeXc9JJBJZ0sanX0yae5np2D4VUDNumkiRG1U3pES7wBzgT5AqnPbIkCysghRi5iUomm4H03D/ZjcPpKTWflsSMxkXWIGi/alMXf3GXwtig51A7B2MtPIX3vsCIotW7YAeNQyXMpsRg0YiW7UHMfsz3F89Bqq7zBU594oP393hyeEEEK4R3AIqls/yMuFvDwsOdm0ST9Dm4h0pqbO49iJdDaYIlkX3pIfghvwvd1M4JlMei49wJC2dWnqnQP+gSiL563i4YntESGqW3k7JboCo2w2m8NqtWoAm82WZrVaQ6ouNCGEp4vw92JY01CGNQ0l1+5ga3IW6xLP8tuRDO6dtZ2GdXwY1zKMXvWDsZg8q3NixIgRACQmJro5EleqfmNMt92P/ukb9C/z0b/Mh/AoVFx9VHx9iK0PUTEoU83O6yGEEEKUh6qbgLrur2W+H596grh9O7ly707OHvmVTV7RrA9swKpjPiw8dojW+SmMO7qCjgN7Yure16M+Pz25PSJEdSlvp0Qy0ATYXbjBarW2Ag5XRVBCiJrHx2IqSjx1S6coNp7UfLL2ENNXH+ezzSe4skUYQ5qE4mvxjKkdbdu2dXcI56V8/WHcDajOvdCH90PiYfTeHegt64wCXt6o2ASIq4+KawBx9VCBwW6NWQghhHAHFR5pTIPs2peg/Dz6HNpL7707yd83h58KovkxtB1PN7yGeluPc9Xq/9F3UBcsHbp5xGhOT2+PCFEdytsp8R9gjtVqfQ6wWK3WScAjwPPl2dlqtX4IjAZSbDZbG+e2MGAm0AA4CFhtNttp53sPAzcDBcBdNpttQXkvSAjhfl5mE6NaR9AlysT6xLN8v+MU729IYebWk4xoVodRzesQ6uvexX/mz5/v1vOXh1IK6jVG1WsMgNYazpyCxIPoxENw9BD6t5/RDoexQ2gYKrae0VEREw9mk5EFyOEAtPFTa+fDAY5izwt/Wrwgsi6E1EGZPKMDSYjKIu0RIS5/yssbmrRCNWlFbEgIVy39iRGr5rGyIIxZda7g9cAhfLHxNGOOrmbo8O74e7l31ERNaI8IUdXK9a3AZrN9aLVaTwG3AUeAG4FpNpttVjnP8xHwBvBJsW0PAUtsNtvzVqv1IefrB50jMK4FWgOxwGKr1drMZrMVlPNcQggPYVKKrvFBdI0PYueJLL7fcQrbtlRm7TzFoEYhjG0ZRt2gmpcp212UUlAnHOqEo9p0AkDn50PyUXTiYaOz4uhB2LGJS17F3dsbFRUL0bGo6FiIioXIupW6Ioh2OIyOFiGqz0dIe0SIWkN5eaE69cKnQ3cGbV1P/1VL2Jjty6zQDsxIj8X2/T5GRhYwKt6LOk2buDtcIWqt8q6+0c3ZATGrxPauNptt7YX2t9lsy61Wa4MSm8cC/Z3PPwZ+Bh50bv/KZrPlAgesVutejJwWv5YnViGEZ2oZ6U/Lfv4cTctl1s5TLNqXxoK9Z+iREMTVrcJpEu7r7hBrJOXlBfENUfENgX4A6Iw0SDnuLKBAmcCkAAUmk5GIXJmdP01GGZOzXG4OpBxHpxyD5GPobRvQG1b/ecI6EajouhAVi4qOg+i6EBJWNARW2/Mh8yxkZhT91IWvs86euy3rLGeCgnFExUJsPVRcfWMaim/1JfXU9nzjmnNyIDcbcnOdP3PQOdlGUrWCAlRCQ6jf2COTpInyk/aIELWTMpuhQzfM7brQZccmOq9cxK5jDmZFdOWbvFhmHc1mwKr5XNW3NXGNEs57LO0oABTKZEIf2oveuh5OpqBTU4zcF9fe4lE5K4SoCco7fnoRUNpk5flA2EWeO9pmsx0HsNlsx61Wa5RzexzwW7FyR53bhBCXgfgQH+7sXpdJ7SKYs+s08/ecYdXhDNpF+3NVyzA61A3AXA1JMTt27AjAxo0bq/xc1U0FhUDQJeQhjm9QtICa1hrSTkFyYUdFIjr5OPyx9c/RGD4+4BdgdDrk5ZV+TC9vCAhEBQQZnRix9SAgEC9HAfl7dqL37vzzeOFRqLjCTor6EFW3Qg087XBARpox1SX9NPrMKefzM5CThS7sgMjLBbu9fMcE8PJCNWiKatISGrdAhYaXOybh0aQ9IkQtoUwmaNMRWnWgxZ7tPLhyEYlHfmF2YBuWhbZk0eoMuq/+hbG9W9CifhQkHUWvXwWpyeiTKZCaAqdPYvrXaxBbD71/F3r2FxASBiGh6GVzwc8fNe6Gcsd0ObdHhCiv83ZKWK1WE857aVar1XmbrUhjoHytuYop7dtIqeN7rVbrbRhTSrDZbERERFRqIBaLpdKPWdNJnbiSOindheolAmhery5/7Wtn9rYkZv5+jCd/PkodPy/6Ng5nQNMIrogLxmKumrwGycnJRhzV+LursX8rkZHQpPk5m3RuDvakRAqOHaHg2GF0dhYqKBhTQDAqKAhTYDCmwOA/t5Ux7cNisRBot+PIzqLgyEHsh/djP7wP++F9OHZsAozRIJb4BljqNcJcvzGWhIagHThOp+I4dZKC0yeLPU/FceaUM4/Gn0zBIZhCw1HhkZh8/VB+/igfX5SvP8rPD+Vb+PA3fvo5n/v4QEEB+ft2kf/HFvJ2bsGxaDYsmo05qi5eLdri3aIdloZNjVErlaDG/p1cfqQ94sGkTlxJnbg6b51ERaF79id01zYaLfqByQe/Yo5vU+bVac+vq04TsyWLPoE5dPx5Jc0tWXhF1cXcqj3myBj8YuMwR0Sgr5wIY69FeRufcenvT8ercQv8KvB7kPaIZ5A6cVWddXKhkRJ2/vwALtkB4QCeuYRzJ1ut1rrOuxJ1gRTn9qNA8XFT8cCx0g5gs9neBd51vtQnT568hHBcRUREUNnHrOmkTlxJnZSuIvUytL4vA+IbsjYxg9WHM1jwRzKztyUR5G2iW0IQPROCaBcTgJe58kZQbNiwAaBaf3eX3d9KQAg0DYGmbcou4wAyMoxHKc6pk7Ao49Gh+58jNBIPoxMPYk88jP55PhSUMZ0/KAQVUsfIfdG0jfE8NOzPu1cWLyqUCMABZGUbD4DIWIiMRfceBqdOoPfupGDvTnJ/WQBLfzJWQ2lYOIqipXH+i1QVfyexsbGVerzLjLRHaiCpE1dSJ67KVScRddHX3kbo4X1MXrmIcXs+ZXVIM35ztOb7jHC+7ngn4T6KHvWC6dkghBYRfuSg4JzjOj/jrrqRPCDz5Em0o6Bco/ykPeIZpE5cVWd75EKdEg0x7hT8AvQttl0DJ2w2W/YlxPQDMAVjBY8pwOxi27+wWq3TMRJLNQUumLdCCFGzeZkVveoF06teMLl2B5uOZ7L6sNFJsXhfGgFeJrrGB9KjXhBX1A3A+xJHUMTExFRS5KIqKKUgNBxCw1GtrwCc+R+Sj8Pxw2CxODscwiA4FGWpntVclFLG9JLwKOjWD52bC4f2GNNP9v2B3r3dKBcRDa06oHoPkVVMPJ+0R4So5ZRSUL8Jqn4TAo4eYPD23xmcvJmzx5JZb6nLrwENWZCTwJw9adRR+XQLyqNXQhCtmsZhCQhwOZ5j3Qr0/G8x3f8syu/8eZKkPSIEqPJkPrdarffbbLb/lLL9XpvNNr0c+3+JkUQqAkgGHsdImmkD6gGHgQk2m+2Us/yjwF8wRmfcbbPZ5pXjWvSxY6XewLho0mPmSurEldRJ6SqrXvILHGxOymLV4QzWHM0gM8+Br8VE17hAetYLomNsAD6WmvGlT/5WXF1OdaK1hpPJ6H07Yc8O9KF9qHZdUKMnVqhjogrvTFR9shYPJ+2Ry4fUiSupE1eXWifa4TByEiUnknXsGBuSc1idE8AGr1jyTBaCC7Lpln+cngHZtKkbgFeTVqi4euhdW3FMnwbtu2K64+GiZNCeQv5WXEmduKrO9kh5OyXSbTabS6JLq9V6ymazXWyiy8omjYBqIHXiSuqkdFVRL/kFmq3Jmfx6JIPfjpwlPbcAH7OiV/1g/tolukKdEw888AAAL774YqXGeD7yt+Lqcq4Tx/L56OULUZ17oYZdXe5GqXRK1HjSHqkGUieupE5cVVWdZKelsXHXcVYfy2J9pg85mAksyGFoxh9c36sR5g5dcSyajbZ9gLr6Rkwjxpd5LGmPeAapE1fV2R65UKLLgYXlrFbrgBIHaETRBCohhKh6XmZFx9hAOsYG8tcumu0pWaw8lMGCvWfIzCvgwT5x5V654/PPPweqtxEgahfVZxjk5aF/+9lYfWTgaI+7WyaEEKLi/EJC6NU1hF5AXoGD349nsmzvKb5L7EDBqs3clJ2JGnwlHNiN/v4zdL3GRVMRS5L2iBAXzinxgfOnD/Bhse0aY9jj36siKCGEuBCzSdEuJoB2MQEkhHjz/oYU3lmXzB1do8v1xe+FF16ohihFbaaUgkFjIC8X/esy8PZB9Rnq7rCEEEJUIm+ziW7xQXSNC+TdtceZvbc9Iet+Y1zWHLjxTjh+BL3vjzI7JaQ9IsQFOiVsNltDAKvV+onNZruxekISQoiKGdMijDM5BXyzPZUQXzOT20decJ/rr7++GiITtZ1SCoZfA/l56F/m4/D2wdStn7vDEkIIUcmUUtzSpS5peQ4+oTshW5cxMGs26sHnMfmWnexS2iNCXHikBAA2m+1Gq9XqBXQHYm0220yr1RrgfC+zKgMUQojyuL59BGdy7Ni2pRLqa2FU84tfklGIyqRMJhhzrdExsWg22ssL1bGnu8MSQghRycwmxT09Y8nIPcqb9Cd493w6Z3+JHncjHNmP3rAaNf4mmconRAnlygpntVrbAruB9/hzSkc/zp3SIYQQbqOU4m9dY+gSF8h765NZeSj9vOUXLlzIwoULqyk6Udspkxl11Q2oxi1w/PQNeusGd4ckhBCiCniZTTzcL46GYb68VHc4fxw6gf7yHfSWdeiF36OXzT2nvLRHhChnpwTwFvAvm83WAsh3bvsF6F0lUQkhxEUwmxT/7B1Li0g/Xll9jM1JZQ/kmjp1KlOnTq3G6ERtpywW1PipqPqNcfzwBfqPLe4OSQghRBXw9zLzr/4JhAV482zCWI4knUGnHIfWHdG2D9C7txeVlfaIEOXvlGgNfOZ8rqFo2oZfVQQlhBAXy8di4rF+8cQGefPcL4nsO5VTarnBgwczePDgao5O1HbKywtlvQUVWw/Hd5+g9+50d0hCCCGqQKifhScGJmD2svBkw/GcTM8Gk4I6ETjeeQF9JhWQ9ogQUP5OiYNAp+IbrFZrV2BvZQckhBCXKtDHzL8HJhDobeKJZUc4npHnUubjjz/m448/dkN0orZTPj6oa29FRcbg+GYG+tA+d4ckhBCiCsQEefP4gAQyHSaeajyRjHwguA7kZKMX/whIe0QIKH+nxDRgrtVqfQLwtlqtDwNfA49VWWRCCHEJwv29+PfABBwa/r30CKez7e4OSYgiys8fdd1fITQMx8z30ImH3R2SEEKIKtAozJdH+sVxPAeea3Eduf7BUDcBOssseCEKlatTwmazzQFGAJEYuSTqA1fbbDbJyiKE8FjxIT5M6x/P6Ww7Tyw7QmZegbtDEqKICgjEdN1fwT8Qx5fvoJMS3R2SEEKIKtAuJoD7etVl15kCXm51HfawSPRX7+LYsBq9eZ27wxPC7S64JKjVajVjrLzRymaz/a3qQxJCiMrTPMKPh/rG8fTPR3l2eSKPD4jH22wiLi4OgMRE+SIo3EcFh2K6/g4cH7+B44u3Md14J0REuDssIYQQlaxnvWBu71LA2+uSebvtZO7c/S360zdI+PoXAI7u2YXyD3RzlEK4xwVHSthstgKgAPCt+nCEEKLydYwN5K4eddmWnMUrq49T4NDuDkmIIio0HNP1d4BSOD5/m4JTJ9wdkhBCiCowolkdrm0bztLDWXzW/lpo363oPcc/rqPgnzfhWLUEAJ2bi967E51V9kpiQlwuLjhSwulVwGa1Wp8FjuJcgQPAZrPtr4K4hBCiUvVvGEJaTgEfbkzhPZ9kjh49ilLK3WEJAYAKj8J03V9xfPomGW+/hJ56D8rLy91hCSGEqGTXto3gTE4B3+06Q2jX6zj8WjBs/A0sFqgTDqFhRsEj+3C88JDxPDQcYuuhYuuh+g5D1Y1Hay3tGHHZKG+nxBvOn0NKbNeAufLCEUKIqjO2ZRhncux8t+MUoX4Wrm0rw+SF51DRsZiuux2/vGzs0iEhhBCXJaUUt3WONm6UbEolpOfV9OvUC73ge/TJZNj0GzqqLsTWw3TnY+hjh+HYYfSxw+hf5qE69jAOtGEVBb8uwzRhKiom3r0XJcQlKlenhM1mK+8qHUII4dFu7BDJmRw7X245SaivmeFN67g7JCGKqNh6+EREkHHypLtDEUIIUUXMJsW9veryxLICXv/1OMH947nitvthw6/oX+ah330J1bkXqu9wTO27Fu2nHcUSdvv4we5tOP79d9SAUajR16ICJCeFqJmks0EIUasopfi/bnU58eW/uO+OW1h39Ky7QxJCCCFELeNtNvFI3zgOfzqNG6fcxIEz+Zi69Mb0t4dRHXug16/C8b9n0RtWoR0OAJTJjDIZg9RV206Ynnkb1Wswesn/t3ffcVLV5x7HP78pO9t732VZmhQFKRYUJaiIioma3OToVaLxakyiMSomlhhNYm4Su5jEmGIUElFzriaxIzZsCCqooBRF+lZ2l7a9zLl/zIDgLH13zu7O9/167Wtnz5wz88zDgX14zu/8fs8QvOl7O+ejEOlt9mukhGVZPuBy4CtANrDzBibbtid2T2giIt3D5zGs++BtAO58q4xfn1rCkKwEl6MSERGRWJIU56ViyXwAbp23kTtP609OUjLmjG/ijD0eZ+6/Cb7wJGbRfDjtG5j+g3Y73qRmYL59Bc6kqQT/+SA0NwFovgnpdfZ3pMS9wPeAN4BxwJNALvBqN8UlItKtHn74Yf7w57+RFu/lf+dtpKq+1e2QREREJMY8/PDD3PGHv9LSHuTW1zZQ3xq6RcPkFWKmXY7nvy7CaWkm+I/7CT45C2dLXcRrmH4D8Fz7v5iTpgLgzH+Fjt/dilOxMaqfReRg7W9T4hvAGbZt3we0h7+fA5zUXYGJiHSnKVOm8PWvns4tJ/WjLehw62sbqW/p2PeBIiIiIl1kypQpXPD1qdwwsYiyba3c/kYZbR2hhQ6NMZjhR+L5/g2YiafhfPYJwT/dRvD1OThtu19MMcZgPOH/2gWDsGoZwV9eSfDxv+I0bI/2xxI5IPvblEgENoQfN1mWlWjb9gpgTPeEJSISHf3SAvx0YjGV9W389o2NtHUE3Q5JREREYsyR+Un8cHwBS6oauX9hBY7j7HzO+P14Jp4Wmm/isCNw3pxL8IHbcFYt6/S1PCdOwfO/f8KccCrOq88RvOn7BBe+Hq2PInLA9rcpsRw4Ovz4feAXlmX9DCjrlqhERLrZI488wiOPPALAEXmJ/Gh8Ph9XN/G7BZW7FQIiIiIi3WXXeuTkgWn896hsXluzjceXRq7CZFIz8HzjQjwXXoGJTyD4+IMEX31291U5du6bjmfa5XhumQElAzHxiQCd7ivitv2a6BK4CthxBk8HHgBSgMu6IygRke52/fXXAzBt2jQAvjIgjU0N7fzjo03kJvn59ugcN8MTERGRGPDleuTcI7Koqm/j8aW15CT5mTwoPeIYUzIILr4a5v4bZ/6rsHEtfP1CTEpq5L7FpXiuuXXnz87Tj7GtvQ3nnG9jfPv7X0GR7rVfZ6Jt2+/t8vgzYHK3RSQiEgUXXHBBxLb/OjyTqoZWnviklrxkP1MGp0c/MBEREYkZX65HQkuX51PX2MYfF1aSnehndEFSxHHG78ecaeGUDCT4/P/hPHgXnnO+jRkwJHLfXVfiMIamF/8N61fj+f71mMTkLv9MIgdqf5cEPXkPT7UAG23bXtd1IYmIdL877rgjYpsxhu8fnU9NQzsPvFtJVoKPcUX6ZS0iIiLdo7N6xOcxXD+xiBvmrue2N8q4bUoJpRnxnR5vRh6FJ7+Y4JMzCT76J8zE0zATJn8x6eWXeM6+gKQBg9n2x9sJ/vY6PFfejMkt6NLPJHKg9ndOib8BL4S/Htnl8ePAKsuyFlmWFdmWExHpZbwew09OLKQ0PcAdb5Wzuq7Z7ZBEREQkxiT6vdxyUjEJfg+3vraR2sa2Pe5rcvLxXHwNZsQYnNfn4Dz+V5zG+j3un3DymaFbOrZvJXjHDTgtqnXEXQfSlPgdkG7bdiGQDtwH/Cn8+D3gj90Qn4hIt6isrKSysrLT5xL9Xn42qZjkOA+3ztvIpoY9FwIiIiIiB2tv9Uh2op9bTiqmsS3Ir+ZtpLFtz5NUmkAAc84FeM74Js66VQT/ejfOxjV73n/oEXh+eifm3O9iAp2PwhCJlv1tSlwF3GjbdhNA+PtNwNW2bTcA1wJHdU+IIiJdb9y4cYwbN26Pz2cl+rnlpH60tAf51WsbaWjVbNUiIiLStfZVjwzIiOe6EwtZt6WF298spz245xXCjDGYccfj+c5V4PUS/Pv9BBe+vsdVxUxuIZ6jTwDAWTSf4L/+jhPU0ugSffvblGjgiyVBdxgHNIYf6+wVkV4lLy+PvLy8ve7TPz3ADROL2LithdveLKOtQ0uFioiISNfZn3pkbGEylx+Tz4cVDTzw7r6XLjcFxXgunY4ZMgLnpadwnpiJ09y412OcVctwXniC4J9vx2lpOeDPIXIo9ncdmFuAuZZlPQ1sAIqBrwFXhp8/BXii68MTEekeixcv3q/9jsxP4ofjC7jvnQr++G4FPxpfsPss1iIiIiIHaX/rkVMHp1Pd0Ib9cWiFMOuI7L3ub+IT4ZsXw8LXcV59FufBe/B88zuY/OLO97cugcwcnP97iOCdN+L54U2Y9KwD/jwiB2O/RkrYtv134FhgBZAGfAocF96ObdvP2rb93W6LUkTERScPTOO/R2Xz6uptPL60xu1wREREJAadPyqbSQNSmf1RDfPWbN3n/sYYPOMn4fn2FdDRQXDm73AWz+90pIUxBs+pZ+O54iao3Ejw1z/G2b6tOz6GSIT9HSmBbdvLgGXdGIuISI917hFZVNW38fjSWpLjvHx1aIZGTIiIiEjUGGP44bEF1Da28/sFFST4PBzbL2Xfx/UbgOfSa3GeeoTg809QX1WGM+lMTEJi5L5HHoPn+ttxPlqISUntjo8hEmGPTQnLsv5i2/Zl4cf/ADq9ecm27Qu7KTYRkW5z+umnAzBnzpz92t8YwxXH5rOtuZ0HF1XzUWUDVxxbQEbCfvd2RURERHZzoPWI32u4YWIRN7+8nt+8UcaUwWlcPDaXRL93r8eZpGQ47zJ451Va336Z4KfL8JxzAab/4Mh9+w3A9BsAgLNhDc7yjzCnnq2LMdJt9lZN77qGzKruCsCyrGuASwk1PZYCFwOJwD+BUmAtYNm2vbm7YhCR2LN06dIDPsbnMdw0qZjnVm7m7x9u4srn1vCDo/OY0F9XEkR6O9UjIuKGg6lHkuO83HFafx5dUsO/l9XxUWUjV40v4PC8yJEPuzIeD2bCZNLGHkvtw78n+I8/Yo4/GTPxdIyv8/8WOm+/jPPKM7D+c5whh4PHg8kvxgwZEXp+8TvgMeDxhr68XsjMweQVhlby2LgG4uIhEA+BAMTF7/G9JHbt8Yywbfu3uzz+ZXe8uWVZRcCPgBG2bTdZlmUD5wEjgFds277NsqwbgBuA67sjBhGJTS+88MJBHecxhq8Ny2RMQRIz3qngjrfKmbihnsuOziMlsPerFCLSM6keERG3HGw94vd6uGhMLscUJTPjnQpuenk9Zw3LYNroHOK8e5820NdvAJ5LrsV56T8481+FNZ/BOdMwWTkR+xrrEkhIwnnun7Dw9dDGE07d2ZQI/uk2+NIcFWbyWZhzL4W2NoK/uibyNc+08JwzDad+G8Hbr4dAws6GBYEAnuNOwRx5NE7DdpxXnws/F25oBOKhdDAmKxentQVqN4WeD8SHjvf5NKKjF9prm8qyrAnAWbZtR/wCtizrNuA/tm0v6IIYEizLaiN0RaIcuBGYFH5+FjAPFQEi0oVGjRp1SMcXpwW4fUp/nvikln8ureHj6kauHJ/P2MLkLopQRKJM9YiIRN2h1iPDcxOZMXUAsz6o5qkVm1lc0cDVxxUyOCt+r8eZQADz1XNxBg0j+JyN8+BdeKZ8HUYfu9t/6o3Hgzn7fJxTz4LWFggGwR/Y+bznlhmhbR1BCHZARwekZ4Se9PnwXP5TnJZmaG2GlhZoacIMHvHF6xeVhpoLLc2wfSvUNOPUb8UAbNmM8/Sju8XtAObCH2JOnAJl6wj+5se7f7CUNDwX/SjU1GhugtYWTGr6gSdWompfY2d+CvxxD8+9DtxEaGnQg2LbdpllWXcB64EmYK5t23Mty8qzbbsivE+FZVm5B/seIiLdxesxnDsym6OKkrl3fjm/fG0jpw1O5+KxuST492txIxHpAVSPiEhvluD38P1j8jmmOJnfL6jkuhfXYh2RzTePyMLn2fuoATP8SDxF/XGeeYzgczZm1XI408IkJu2+X2IyJEZeeDHFA/b82l4vjBnPniIwyamY7++5z2uKSvD8+d+hZkhLuHHR2gxp4aVKs/Mxl14bfr459FVdAeERH86S93D+ehdk5kDpEEzpEMyAITBoGMYft9e8dCXHcaC2GmfVMszgEZjsvKi9d2+xr6bEaGBPs668BPztUN7csqwM4GxgALAF+D/LsqYdwPGXAZcB2LZNdvbe1+s9UD6fr8tfs7dTTiIpJ53r6Xn51a9+BcDNN998yK+VnQ2zBhby13fW8fjiMpZuauZnpw7hyKK03fbr6Tlxg3ISSTmJPtUjvY9yEkk5idQbctKV9ciU7GzGH1bMPfM+57Glm/igqpmbTzuM0szd55qIyEt2Ns6VN9H8xlyaXngSM/M+ks+7FP9hI+jRsrNhwMA9Pt0++mhaLvohbauW075qOR2L5+MA2X/5F97sbFo+XEhH2Xp8g4fjSU4kKyury279CDY20Pza87QuX0Lb8o8I1oWWlE/+7nQShx1O06vP0bp8CSnfuRJPUs8cZRvNvz+ms3Vqd7AsazuQa9t2UyfPJQDVtm3vex2aPb/+t4DTbdu+JPzzhcB44BRgUviqRAEwz7btoft4Oae8vPxgQ+lUdnY2NTU1XfqavZ1yEkk56VxPz0tRUREAZWVlXfq6n1Q1ct+CCqrr2zhneCbnH5m9897Onp4TNygnkbojJ4WFhcAeL1bFPNUjvY9yEkk5idQbctJd9cjb67fxwLtVNLcF+fboHL42LANP+D/ce8uLU1lG8D//gJpqzPivYCZNxfj8XRqbW5zt22D95zBiNMYYgrMfwJm3y5wegXjILcBz8wyMMTgfLsDZvg2TkQ2ZoS8THzmZqNPcBKtX4qxaDrn5eMafhNPUSPCq8yEjM3S7yuDhoe9FJRiPN3TLzFOPQloGnm9fjhl1dBQzsX+iWY/sa6TECmAK8FQnz00JP38o1gPjLctKJDRc8hTgfaABuAi4Lfy9s/cXETlo06dP75bXPTwvkRlTS5m5eBP/Xl7HovJ6rjm+kIGZe7+3U0RcpXpERFzRXfXIhJJURuQkcv/CCh5aXM27ZfX8aHw+ecl7v23B5BfhuWQ6zsvP4Cx4/YtJMHPyuyXOaDIpqXD4mJ0/ey74Ac5UC9Z9RmLDdho2roeOtp2jJYKvvQDLPmC3S/glA/HePCP0/AtP4ix6GzasDs2rYUxorovxJ2ESEvHc+TAmLaPTWDxnWjgjxhCceR/B3/8KM/4kzHmXYpIO+np/r7avkRLnA/cAlxOa1DJoWZYHOAe4H5hu2/ZjhxKAZVm/BM4F2oEPCC3HlQzYQAmhQuFbtm3X7eOldGUiCpSTSMpJ55QXeL+snj8srGRbczvnjczmsq8MZUtdrdth9Sg6TyJppIQ7VI/0LspJJOUkknISms/gldVbefD9agAuGZfLeccOorZ23/WI8+knBJ99PDRZ5ClfwxwxDpOw92VHe6vOzhWnvQ221EFdDc7mGthcAz4/nslnAdBx509DjYgdoyAGDo2Yi2NfnLY2nOdtnBeewHPFTZiRR3XZZzpU0axH9tqUALAsazrwSyAA1ADZQDPwc9u27+3SKA+NioAoUE4iKSedU15Ctrd08Of3Knlz3XbOHJHLZWMy3Q6pR9F5EklNiV5P9UgUKCeRlJNIyskXqupb+d2CSj6uauR7x/dn6oCE/TrOqd+G88zjOJ+HB8hn5WCK+mOKSqGoP+TmYzy9f0l0t88Vp7YakxWaS9lZ/A4MGYFJSdvHUd2rJ92+gW3b91iW9SBwHJAF1ALv2La9rUsjFBGJoiVLlgCHvhTXvqQEvPz4hCLykjfxxCfVHJbuZdIAd3/JiIiISM8QrXokLzmOX53Sj7vfLufBd9YxIKmE4bn7HvVgklPhvO9i1q/G2bgGNq7D+XwFzpL3Qzv4/ZiCflDUH1PUH4pKQ7dJyAHZ2ZBoqCf40IxQXs//HuaoE7ps8s2ebJ9NCYBwA+LFbo5FRCRqzjjjDKDrJ5bak/NHZfPZ5jYeeLeSIVkJFKVGbykqERER6ZmiWY94jOGKY/NZvaWNu94uZ8bUAaQE9j3KwRgD/Qdh+g8Cwktcbq2DsvU4ZWtDjYqFr+MEg6EDUtMxxeEGRU4+GBOac8Fxwl9BcIBgR2j/iOcciAtAbiFkZGE8sbPMuklKxnPjnQRn3ofzlztx3nsTzwU/2OPcFH3FfjUlRET6mpEjR0b1/bwewy9OH8qFjyzizrfKuOO0/jtX5RAREZHYFO16JNHv5dYzhvJ9+yN+t6CCn04sOuAr8cYYSM+C9CxMeOJIp70NKstwytZB2brQ92UfsfeJAvaD34/JKYC8QkxeIeQVQW4BJtB1E4g7bW3sa0qDaDJFJXhuuAPn5adw/jOb4C9/hOc3f+505Y++Qk0JEYlJc+bMifp75qYE+NFxBfz69TJmLq7msqN7/0zWIiIicvDcqEeG56Vw4ehcHlpczbMrN/O1YYc+35Xx+aG4FFNcunObs30b1G0KzSDg8QAGjAc8JjR6wnjC380Xz3s8of0bG6C6AqeqHKrLcZZ/hPPBgi/eMD0z3KQoDDctikLbPJ7QiI2mRmish4bt0FCPE/6+42cad9nW1srmlFSCuYWhJTsL+4duR4nSpJ5ORwe0NIe/mnY+NoUlON+4EDauxXn7FYLBIGxYjRlwGCa/GHLyQ8uU9oE5PdSUEBGJomOKU/jasAyeWbGZkflJHNcvNpd+EhEREfecNSyDpVUNzPxgEyNyExnUDUuXm5RUONj5JdKzoLBk54yIjuPA9i1QWY5THW5UVJXDp598McohLg78caGGRmcjH4yBxGRMUjIkpWAysiAxGRKTiGtrpv3T5aH5Mnbsn5mNKSrFFJWEJ/UsxHj3vwHgtLSEbnPZUoezdfPOx2zbgtPSBM3hJkR7+75fq6os1KzYuBbnnde+iNHrxZz/fTwTT8PZvhVnwbzQLTM5BZCTh4kL7He8blJTQkQkyi4ancOy6iZ+v6CCQRnx5Cb73Q5JREREYogxhh+NL+Dq59dy51tl3HNGKYn+nnvF3RgDqRmQmoE57PCd2522VthUCVXlof+4t7dDUgokp2ASkyHcgCApGeIT9zg/RXJ2Ns01NTgtzVC+Aad8XWiujNUrcJaGJ/X0+UIjFHZM6plfDK0tsHUzzo6GQ7j54Gypg+am3d/E54O0DExqemiOiEA8xMdDIAHiApj4hNC2QAIEAqHv8fEQiMf4/DitLTirP8X5ZBF8+kno/drbcN57k2BrC/h8OPbfdr9lJj0Tc8Y38Zz81a79A+liakqISEwaO3YsAIsXL476e/u9Hn5yQiHXPL+Wu94u5zenluDz9P2ZlUVERGR3btYjqfE+rj2hkJ+9vJ4H3q1i+vEFvW6lB+OPg8KS3UZVHNLrBeJhwBDMgCHAlyf1XAfl63AWvY2z8PXIg32+UBMgLRNTWLLzMWkZkJ4ZGqVxCJN2mrgAZthIGDYyFFdNVWip1s+X47z7BnR0wMDDILsAk5oGPn/oNpAJkw/6PaNFTQkRiUlVVVWuvn9BShyXH5vP3W+X8+hHm7hwTK6r8YiIiEj0uV2PHJ6byHkjs3l0SQ1H5icyeVC6q/H0NJ1P6tkO1eVQWQbxCaGGQ1pGqOkQpaaOMQZy8kO3aoyfFLpVZO1nOKuWhW5BqQqt5mJyC3A+XIgz6ij4z2zM16f1yAkz1ZQQkZi0aNEit0NgYmkqS6saeHJZHSPzkxhTkOR2SCIiIhJFPaEe+ebhWXxc1cif36visOwEStJ6xzwEbjE+387RGT2FCQRg6BGYoUeERlFsqsRZtRw+XYrz0lOweiXMex5n7Wd4rvp56NaWHkTr0YlITMrPzyc/3/3VLy4dl0dJWhz3zi9nc9O+JzoSERGRvqMn1CNej+GaCYUk+Dzc9WY5Le1BV+ORQ2OMweQW4Dn+ZMyFV2JGHwOfr4BJU2Hd5wTvuSW08kgPoqaEiIiLAj4PPzmxiKa2IPfML6cj2HPWyRYREZHYkJng4+rjC1i3tYW/Lap2OxzpIsbjwUy1QreefL4CTj0bytYRvOsmnO1b3Q5vJzUlRCQmXXfddVx33XVuhwFASVqAy47KY0llI08uq3U7HBEREYmSnlSPjC1M5hsjMnlx1RbeWrfN7XCkixiPB3PW+aFVS5Z/hJn6TWhthaZGt0PbSU0JEYlJs2fPZvbs2W6HsdPkQWlM7J/KY0tqWFbdc35JiIiISPfpafXIBUfmMDQ7nvsXVlK5vdXtcKSLGK8X840LMQMPw1nyPua8S0OTYDoOTr37DSg1JUQkJt1+++3cfvvtboexkzGGHxybR26Sn7veLmdbS4fbIYmIiEg362n1iM9juHZCIcbAnW+V09ah20r7CuPzY771P5h+A3CeeRzn049x/vV3gr++FqfG3VVg1JQQkZg0bdo0pk2b5nYYu0n0e/nJCUVsbW7n9wsqQrMni4iISJ/VE+uRvOQ4rjy2gFV1zfzjQ80v0ZcYfxzm3EsxeUUE//V3yC+CxnqCd/4Up7rctbjUlBAR6UEGZ8XznTG5vLuxnmdXbnY7HBEREYlBx5WkcMaQdJ5asZn3y+rdDke6kAnEY87/HiYzB+etlzDTLofW5lBjomKjKzGpKSEiMWnu3LnMnTvX7TA69dWhGRxTnMzMD6pZVdvsdjgiIiLSTXpyPfI/43IZkBFgxjsV1DS2uR2OdCGTkIi54PuQkoYz73nMhVdCRwfBGbfgtEV/LhE1JUQkJl188cVcfPHFbofRKWMMV44vIC3ex51vlbGksoFtze1uhyUiIiJdrCfXI3FeDz8+oZC2jiB3v1XO8upGGts051VfYZJS8Ey7HBKScF55BnPJdDzTLsf446Ieiy/q7ygi0gNMnjzZ7RD2KjXg5ccTCrnllQ3c/MoGADISfJSmByhND9A/PUBpRoDi1Dj8XvWXRUREeqOeXo8Upwb4wTH5zJhfwQ0vrQcgN8lPaUaA/mmhWqQ0PUBBShxej3E5WjlQJjUdz/nfJ/iPP+DM/XdoxAQQfPcN2oYfASmZUYlDTQkRiUmzZs1yO4R9GpGbyENfH8TqzS2s29LC2i3NrN3cwjMrG2kPhibB9JpQwdA/PUD/jC8aFtmJPoxRcSAiItKT9YZ6ZNKANEbmJbJmcwtrN4frkS0tvF9WT7gcIc5r6JcWR//0+N0unqTH67+bPZ3JzA43Ju4nOPsBzPnfgw8X4v3KqdDUEpUYdJaIiPRgqfE+Rhf4GF2QtHNbR9ChbHsr6za3sHZLC+u2NLN8UyNvrPtinem0eC/njczmtMHpunIhIiIihyQr0U9Wop+jipJ3bmvtCLJxa2u4Fmlh7eZmPiiv59XVW3fuk5/s58IxORzfL0UXS3owk5Mfakw88kecfz6I58Ir8SSlqCkhIiKd83oMJWkBStICnLjL9vrWDtZvCTUq5q/fzp/fq+Llz7fyg2PyGJKV4Fq8IiIi0vfEeT0MzIxnYGb8btu3NLeHmxQtvLp6K3e8Wc7YgiQuOzqPgpToz1cg+8fkF+E577sEH/0TzuwHCF59c9TeWzcii0hMKioqoqioyO0wulRynJcRuYlMPSyDX53Sj2snFFLX2MZP5qzjT+9WUt+qyalERER6kr5Yj6TH+zgyP4mzh2dyzxmlXDIul2Wbmrjy2TU8vqSG1o6g2yHKHpjiUjznXoqzpZbts+7HcZyovK9GSoiI9EHGGCaWpjKuMIlHl9Tw/Kebmb9hOxePyWXSgFQNoRQREZFu5/UYzhqWyYSSFB5aXM1jS2uYt3Yr3zs6nzG73JoqPYfpPxjPNy8mMS+PtijVixopISIxqaysjLKyMrfD6HZJcV6+e1Qed59eSl6SnxnvVPCzl9ezfmt07hEUERGRPYuVeiQr0c9PTijilyf3wwC/eHUDd7xZRm1jm9uhSSfM4OH4BxwWtfdTU0JEJAYMzIzn9tP6c/kx+azd0sLVz61h1gfVNLdrCKWIiIhEx+iCJO47cwDnj8rm3Y31XP7MGp5aXkdHMDq3CUjPpKaEiEiM8BjDaUPS+ePXBjJpQBr/WlbHD59ZzYIN26N2z6CIiIjEtjivh3NHZvP7rw7g8NwEHlpczfQX1rK8utHt0MQlakqISEy66KKLuOiii9wOwxVp8T5+dFwBvz21hES/l9++UcavX99IVX2r26GJiIjElFiuRwpS4rh5UjE3nFjE9tYObnhpPb9fUMG25na3Q5Mo00SXIhKTXn75ZbdDcN2I3ETumVrKsyvreGxJDT98dg3WEVl8Y0QWXo8mwhQREelusV6PGGM4riSF0QVJ/HNpDU+vqGPhhu18Z2wupwxM08TcMUJNCRGJSQ8//LDbIfQIPo/hnOFZnNA/lQffr+aRj2p4v6yB6RMKyEvWWuIiIiLdSfVISILfw3fG5nLSwDT+9G4lv19QyXtl9VxxbAGpAa/b4Uk30+0bIhKTpkyZwpQpU9wOo8fITvRzw8Qirp1QyPqtLVz9/FrmrdnqdlgiIiJ9muqR3fVPD/DrU0v4zpgc3i+r56rn1vBRZYPbYUk3U1NCRER2mliayoyppfRPD3Dv/ArufruchtYOt8MSERGRGOExhq+PyOKO00pJ8Hv4+SsbmLm4mrYOTcrdV7l++4ZlWenAg8ARgAP8D7AS+CdQCqwFLNu2N7sToYj0RY888ggA06ZNczmSnicvOY5fTy7hiU9qeXxpDSs2NTF9QgHDcxLdDk2k26geERE3qB7Zs0GZ8dxzRikPLarm38vrWFLVwPQJhRSnBtwOTbpYTxgpcR8wx7btYcCRwHLgBuAV27aHAK+EfxYR6TLXX389119/vdth9Fhej+Hckdn89tT+GAM/fWk9jy3ZpHXEpS9TPSIiUad6ZO/ifR4uPzafGycWUV3fxvTn1zJ31RYtZd7HuDpSwrKsVGAi8B0A27ZbgVbLss4GJoV3mwXMA/S3VUS6zAUXXOB2CL3CsJwEZkwt5c/vVfH40lo+rGjUJJjS56geERG3qB7ZP+P7pTAkK54Z71Rw/8JKFpVrEsy+xO3bNwYCm4CHLcs6ElgEXAXk2bZdAWDbdoVlWbkuxigifdAdd9zhdgi9RqLfyzXHFzKuMJkH3q3k6ufX8r2j85g0IM3t0ES6iuoREXGF6pH9l5Xo55cn9+Op5XU88tEmrnpuDVcfX8CR+UluhyaHyO2mhA8YC1xp2/ZCy7Lu4wCGRlqWdRlwGYBt22RnZ3dtcD5fl79mb6ecRFJOOqe8ROrtOflGdjbHHVbIrXM+5d75FXxc286PTxpEcuDgf5X09px0B+XEFapHehnlJJJyEkk56Vxvz8t3c3KYOKyIX8xZyc9f2cB/jyvisuP64/ce/MwEvT0n3SGaOTFu3o9jWVY+sMC27dLwzycSKgIGA5PCVyUKgHm2bQ/dx8s55eXlXRpfdnY2NTU1XfqavZ1yEkk56VxPz0tlZSUA+fn5UXvPnp6T/dURdHZOgpmd6D+kSTD7Sk66UnfkpLCwEMB06Yv2IapHeh/lJJJyEqk35ET1yMFrbg/y0KJqXly1hUGZgUOaBLOv5KQrRbMecXWiS9u2K4ENlmXt+AV/CrAMeBq4KLztIuApF8ITkT5s3LhxjBs3zu0weqXOJsF8cFEVq+uaNfGU9EqqR0TELapHDl5nk2D+c2kN5dta3Q5NDpDbt28AXAnMtiwrDlgNXEyoWWJblnUJsB74lovxiUgflJeX53YIvd6OSTD/+n41z63czDMrNpOf7Of4khSOL0lhcGY8xujivPQaqkdEJOpUjxy6HZNg3r+wkkeX1PDokhpK0wM765F+aVpCtKdz9faNLqbhklGgnERSTjqnvETqyznZ2tzOwo31zF+/nSWVDXQ4kJPo47hwQTA0OwFPJw2KvpyTg6XbN3o91SNRoJxEUk4iKSed68t52dTQxjsbtjN//XaWb2oCoF9aHMf1S2FCSQr90wOdXjDpyzk5WNGsR3rCSAkREenl0uJ9TBmczpTB6Wxv6eDdjdt5Z8N2nv90C0+v2ExmQqhBMaFfCsNyEvB69P9jERER6Vo5SX7OGpbJWcMyqW1sY8GGeuZv2M4Tn9Rif1xLYYqf40tSOb4khYEZnTcoJPrUlBARkS6VEvByyqB0ThmUTkNrB++VhUZQvLRqC8+t3ExavJfj+oVGUHwlM8vtcEVERKQPykr0c+bQDM4cmsGWpnYWbAyNoPjXslqe+KSW3KQvbjnNzOozdw/0SmpKiEhMOv300wGYM2eOy5H0bUlxXiYNSGPSgDSa2oIsKg81KF5bvZU5n20h+c1yRuUnMq4wiTEFSWQl+t0OWUREJGpUj0RHeoKP04dkcPqQDLY1t/Nu+ILJsyvr+M/yOjLfLGd0fgJjCpIZXZBEasDrdsgxRU0JEYlJS5cudTuEmJPg93BC/1RO6J9KS3uQxeUNfFzbzvw1tcxfvx2A/umBnQ2K4TmJ+L0aVikiIn2X6pHoS433MXlQOpMHpVPf0sG7ZfV8UtvGwrV1vLp6Gx4DQ7LiGVuQzNjCJAZlxuu2026mpoSIxKQXXnjB7RBiWsDn4biSFL42NptNm9JZt6WFxeUNLK5o4OkVdfxrWR3xPg9H5icypiCJcYXJ5CZrFIWIiPQtqkfclRzwcvLANKxjsqmq3sSqumYWl9ezqLyBx5fW8NjSGlICXsbkJzE2fNEkPUH/he5qyqiIxKRRo0a5HYKEGWMozYinNCOebxyeRWNbB0srG1lc0cDi8noWbqwHqihOjWNMYahBUZoeIDXg1ZULERHp1VSP9Bxej2FodgJDsxP471E5bGtu58PKRhaV1/NBRQNvrNsGwKDMeMYWJDGmMIni1DhSA15NmHmI1JQQEZEeJdHv5dh+KRzbLwXHcSjb1sriigYWlTcw59MtPLNiMwAeAxkJPrISfGQl+shM8JGZ6CcrwUdmom/n90S/7gsVERGRA5Ma72NiaSoTS1MJOg6r61pYXFHPB+UNPLmslv/7pBYAn8eQuVstsqM28e+2PeDzuPyJei41JUQkJt19990AXHvttS5HIntjjKE4LUBxWoCzhmXS0h7kk+pGKra3UdfUTl1TG7WN7Wzc1sqSykYa2oIRr5Hg84QKhEQfAzPiGZadwNCcBDI1/FJERFymeqR38BjD4Kx4BmfFYx2RTX1rB59UNVLdEKpD6praqW1qZ83mFhaV19PcHrmaR3Kch8wEH9mJfgZnxe8clZGiSTXVlBCR2HTPPfcAKgJ6m4DPw9jC5D0+39wepK6xndqmL4qEuvD36oY2nl25mf8srwMgN8m3syAYlpPAgIx4fLodREREokj1SO+UHBca1dkZx3FobAuGGhW71CK1TaELKlX1bTzxSS3BcN+iKDWOodkJDM8J1ST90uLwxNjtIGpKiEhMmj59utshSDeI93koTI2jMDWu0+fbOoKs3tzCik1NrKxpYll1E2+uC638Eec1DM6MZ1i4KBiWnaDJrEREpFupHul7jDEkxXlJivPSLy3Q6T7N7UE+q21i5aZmVtQ08l5ZPa+u3gpAot/DYVnxDM0J1SKHZSeQHNe3R1Oo2hKRmKQrErHJ7/XsHB2xw6aGNlbWNLGipomVm5p4ekUd7eG7QPKS/Ryem8j5o7LJSdLqHyIi0rVUj8SmeJ+HkXlJjMxLArJwHIeK7W2hWqSmiRWbmvi/j78YTdEvLY4j85M4f1Q2SX2wQaGmhIiIxLScJD85SX5O6J8KQGtHkM9rm3cWBm+v28Z7ZfVMP75gr7eOiIiIiBwMY8zOkZ4nD0wDoLGtg89qm1m5KXTh5PlPN/N+WT3Xn1jEwMx4lyPuWmpKiEhMWrJkCaCluCRSnNfD8NxEhucmArBxawt3vFnOra9t5FtHZHHeyGwtRSoiIl1C9YjsSaLfy5H5SRyZnwTA8upG7nyrnOteXMelR+Vy2uD0PrMUqdYlEZGYdMYZZ3DGGWe4HYb0AsVpAe48vT8nDUzD/riWn7+6gc1N7W6HJSIifYDqEdlfw3MTuXdqKYfnJfLAu1XcO7+Cpk5WHeuN1JQQkZg0cuRIRo4c6XYY0ksEfB6uOq6AK8fns7KmiWueX8PSqga3wxIRkV5O9YgciLR4H7dMKub8Udm8sXYbP56zlvVbWtwO65Dp9g0RiUlz5sxxOwTphSYPSmdwZjy3v1nOLa9s4PxR2fzX4Vkxt3SXiIh0DdUjcqC8HsO5I7MZlpPA3W+X8+M5a/nBMfmcFJ6LojfSSAkREZEDUJoRz91n9Of4khQe+aiG/523kW0tHW6HJSIiIjHkyPwkZkwdwOCseGa8U8EfFlTQ0t47b+dQU0JEROQAJfq9/HhCId87Oo+PKhu55vk1rKxpcjssERERiSGZCT5+dUoJ3zw8i5c+38r1c9dRvq3V7bAOmJoSIhKTxo4dy9ixY90OQ3oxYwxTD8vgtikleIzhxrnreHpFHY7juB2aiIj0EqpH5FB5PYZvj87h5knF1DS0Mf2Ftby9fpvbYR0QNSVEJCZVVVVRVVXldhjSBwzJSuDeM0o5qiiZvy2q5vY3y2ho1e0cIiKyb6pHpKscVZTMvVMH0C8tjjveLOev71fR1tE7LpRooksRiUmLFi1yOwTpQ5IDXm6cWMRTK+qY9cEm1rywlutPLGJgZrzboYmISA+mekS6Uk6Sn9+c2p9ZH1bzzIrNrKxp4roTishN9rsd2l5ppISIxKT8/Hzy8/PdDkP6EGMM5wzP4jeTS2jrcLjuxXW8sbZ3DZ8UEZHoUj0iXc3vNVw6Lo/rTyykbFsr019Yw8dVjW6HtVdqSoiIiHSh4bmJ3Du1lCFZ8dz9djmzP9pEUPNMiIiISBQdX5LK3aeXkhbv45ZX1jN31Ra3Q9ojNSVEJCZdd911XHfddW6HIX1UWryPW08pYfKgNOyPa7njzXKae+kyXSIi0n1Uj0h3KkyN4/bT+jMqP4n7F1by4KIqOoI970KJmhIiEpNmz57N7Nmz3Q5D+jC/1/DDY/P5n7G5LNiwnRvnrqOmsc3tsEREpAdRPSLdLTnOy82Tivnq0AyeWbGZX7++scdNyK2JLkUkJt1+++1uhyAxwBjD2cMzKUqN4663yvnxC2u58SvFDM1OcDs0ERHpAVSPSDR4PYbvHpVHSVqAP79XyXUvruNnk4opSIlzOzRAIyVEJEZNmzaNadOmuR2GxIijipK547T+xPk83PTSek2AKSIigOoRia7ThqTzi5P7sbW5nZ+8uK7HTICppoSIiEgUlKQHuOu0/poAU0RERFwzKj+JO08vJS3g5ZZX1vNSD5gAU00JEYlJc+fOZe7cuW6HITEmVRNgiojILlSPiBsKUr6YAPMPCyv5m8sTYGpOCRGJSRdffDEAZWVlLkcisWbHBJglaQEeXlzNjXNbuWlSMdmJfrdDExGRKFM9Im7ZMQHmQ4ureXrFZsq2tXLthEKS4rxRj0VNCRGJSZMnT3Y7BIlhnU2A+dOvFHOYJsAUEYkpqkfETTsmwOyXFsdf3qtybQJM3b4hIjFp1qxZzJo1y+0wJMbtNgHmy5oAU0Qk1qgekZ7g9CEZ/OLkfmxxaQJMNSVERERctGMCzMGZoQkw//LOOk2AKSIiIlE1Kj+Ju3aZAPOZjyuj9t494vYNy7K8wPtAmW3bX7UsKxP4J1AKrAUs27Y3uxehiIhI99kxAeaf3qvk/fVb+NrABDxe43ZYMUf1iIiIxLIdE2De9VY5723Ywvi8LIzp/nqkp4yUuApYvsvPNwCv2LY9BHgl/LOISJcpKiqiqKjI7TBEdtoxAeZ93zgCv7en/HqOOapHRCSqVI9IT7NjAsybTj0sKg0J6AFNCcuyioEzgQd32Xw2sOPmqlnAOVEOS0REJOqMMST4oz/rtageERER2cHrMQR80WsV9ITbN2YA1wEpu2zLs227AsC27QrLsnI7O9CyrMuAy8L7kZ2d3aWB+Xy+Ln/N3k45iaScdK6n56WlpSXq79nTc+IG5SSScuKaGage6TWUk0jKSaTekBPVIz2DchIpmjlxtSlhWdZXgWrbthdZljXpQI+3bfsvwF/CPzo1NTVdGR7Z2dl09Wv2dspJJOWkc8pLJOUkknISqTtyUlhY2KWv19eoHul9lJNIykkk5aRzyksk5SRSNOsRt2/fmACcZVnWWuBx4GTLsh4BqizLKgAIf692L0QRERHp41SPiIiIuMTVpoRt2zfatl1s23YpcB7wqm3b04CngYvCu10EPOVSiCIiItLHqR4RERFxj9sjJfbkNuBUy7I+A04N/ywiIiISTapHREREullPmOgSANu25wHzwo9rgVPcjEdERERij+oRERGR6OqpIyVEREREREREpI9TU0JEREREREREXKGmhIiIiIiIiIi4Qk0JEREREREREXGFmhIiIiIiIiIi4go1JURERERERETEFWpKiIiIiIiIiIgrjOM4bsfQVfrMBxEREekGxu0AYoTqERERkT2LqEf60kgJ09VflmUt6o7X7c1fyolyorwoJ8pJr82JREdvOid67ZdyopwoJ8qLctJrcxKhLzUlRERERERERKQXUVNCRERERERERFyhpsTe/cXtAHog5SSSctI55SWSchJJOYmknMiX6ZyIpJxEUk4iKSedU14iKSeRopaTvjTRpYiIiIiIiIj0IhopISIiIiIiIiKu8LkdQDRZlvUQ8FWg2rbtI8LbjgT+BCQDa4ELbNveZlnWBcBPdjl8FDDWtu0PLcsaB8wEEoDngats2+61Q066MC/zgAKgKfzcFNu2q6PzKbrWAebEDzwIjCX0d+rvtm3/NnxMnzlXujAn84jN8yQO+DNwFBAkdC7MCx8Tq+fJ3nIyj75znvQD/g7kE/qcf7Ft+z7LsjKBfwKlhPJi2ba9OXzMjcAlQAfwI9u2Xwxv7zPnSixTPRJJtUjnVI9EUj0SSfVIJNUjkXpyPRJrIyVmAqd/aduDwA22bY8E/k34l5xt27Nt2x5t2/Zo4NvAWtu2Pwwf8wBwGTAk/PXl1+xtZtI1eYHQX+7R4a9e+Rc2bCb7mRPgW0AgvH0c8D3LskrDz/Wlc2UmXZMTiM3z5LsA4e2nAndblrXj3+BYPU/2lhPoO+dJO3CtbdvDgfHAFZZljQBuAF6xbXsI8Er4Z8LPnQccTiiXf7Qsyxt+rb50rsSymage+bKZqBbpzExUj3zZTFSPfNlMVI982UxUj3xZj61HYqopYdv2G0DdlzYPBd4IP34J+K9ODv1v4DEAy7IKgFTbtt8Jd4P+DpzTLQFHSVfkpa85wJw4QJJlWT5C3cJWYFtfO1e6IifRiDOaDjAnIwj9Q0/4F9oW4KgYP086zUn3Rxldtm1X2La9OPx4O7AcKALOBmaFd5vFF3/uZwOP27bdYtv2GmAVcExfO1dimeqRSKpFOqd6JJLqkUiqRyKpHonUk+uRmGpK7MHHwFnhx98C+nWyz7l88QuvCNi4y3Mbw9v6mgPNyw4PW5b1oWVZN1uWZbozQBfsKSdPAA1ABbAeuMu27Tpi41w50JzsEIvnyUfA2ZZl+SzLGkDoik0/Yvs82VNOduhz50n4Ct0YYCGQZ9t2BYQKBSA3vFsRsGGXw3acE7FwrsQy1SORVIt0TvVIJNUjkVSPRFI9EtbT6hE1JeB/CA1dWQSkEOqg7mRZ1rFAo23bH4c3dXYi9sp7rfbhQPMCoaFNI4ETw1/fjlawUbKnnBxD6D6rQmAAcK1lWQOJjXPlQHMCsXuePEToH+33gRnAfELD6GL5PNlTTqAPnieWZSUDTwJX27a9tyt1ezonYuFciWWqRyKpFumc6pFIqkciqR6JpHqEnlmPxNREl52xbXsFMAXAsqzDgDO/tMt57N6B3wgU7/JzMVDenTG64SDygm3bZeHv2y3LepTQL4K/d3+00bGXnJwPzLFtuw2otizrbUJDvt6kj58rB5GT1bF6nti23Q5cs2M/y7LmA58Bm4nR82QvOelz/56EJ1t7Epht2/a/wpurLMsqsG27IjwUcsd9qhvZ/QrNjnMiJn7/xCrVI5FUi3RO9Ugk1SORVI9EUj3Sc+uRmB8pYVlWbvi7B/gZoRlZ2WXbt4DHd2wLD2nZblnW+PDwnQuBp6IadBQcaF7Cw52yw4/9hGa73fXKRa+3l5ysB062LMtYlpVEaOKYFbFwrhxoTmL5PLEsKzGcCyzLOhVot217WSyfJ3vKSV87T8J/rn8Dltu2fc8uTz0NXBR+fBFf/Lk/DZxnWVYgPIx0CPBuLJwrsUz1SCTVIp1TPRJJ9Ugk1SORVI/03HokpkZKWJb1GDAJyLYsayPwcyDZsqwrwrv8C3h4l0MmAhtt2179pZf6AV8sgfJC+KvX6qK8BIAXw39hvcDLwF+7O/bucoA5uT/8+GNCw5ketm17Sfi5PnOudEVOwv/gx+p5kkvosweBMnYf/her58mectKn/j0BJhD6bEsty/owvO2nwG2AbVnWJYQK528B2Lb9iWVZNrCM0PDRK2zb7ggf12fOlVimeiSSapHOqR6JpHokkuqRSKpHOtVj6xHjOH3tViERERERERER6Q1i/vYNEREREREREXGHmhIiIiIiIiIi4go1JURERERERETEFWpKiIiIiIiIiIgr1JQQEREREREREVeoKSEiIiIiIiIirlBTQkRERERERERcoaaEiIiIiIiIiLji/wEBMt1YNjJ1/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "intercept = -24.75035353644767\n",
    "sc_did = data_piv.drop(columns=\"california\").values @ unit_weights.values\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18,5))\n",
    "\n",
    "ax1.plot(data_piv.index, sc_did, label=\"Synthetic Control (SDID)\", color=\"C0\", alpha=.8)\n",
    "ax1.plot(data_piv[\"california\"], label=\"California\", color=\"C1\")\n",
    "ax1.vlines(x=1988, ymin=40, ymax=160, linestyle=\":\", lw=2, label=\"Proposition 99\", color=\"black\")\n",
    "\n",
    "ax1.legend()\n",
    "ax1.set_title(\"SDID Synthetic Control\")\n",
    "ax1.set_ylabel(\"Cigarette Sales\");\n",
    "\n",
    "ax2.plot(data_piv.index, sc_did+intercept, label=\"Synthetic Control (SDID) + $w_0$\", color=\"C0\", alpha=.8)\n",
    "ax2.plot(data_piv.index, sc, label=\"Traditional SC\", color=\"C0\", ls=\"dashed\")\n",
    "ax2.plot(data_piv[\"california\"], label=\"California\", color=\"C1\")\n",
    "ax2.vlines(x=1988, ymin=40, ymax=160, linestyle=\":\", lw=2, label=\"Proposition 99\", color=\"black\")\n",
    "ax2.legend()\n",
    "ax2.set_title(\"SDID and Traditional SCs\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "953820c7",
   "metadata": {},
   "source": [
    "As we can see in the first plot, the obvious difference is that this new synthetic control is no longer on top of California. That's because we've included an intercept, which allows the treated unit to be on an arbitrarily different level than its synthetic control. This new Synthetic Control method is built to have the same pretreatment trend as the treated unit, but not necessarily the same level. \n",
    " \n",
    "In the second plot, we shift this new SC by adding back the intercept we've removed earlier. This puts it on top of the treated unit, California. For comparison, we show the traditional SC we've fitted earlier as the red dashed line. Notice that they are not the same. This difference comes both from the fact that we allowed for an intercept and from the $L_2$ penalty, which pushed the weights towards zero.\n",
    " \n",
    "Now that we have both time $\\hat{\\lambda}_t$ and unit $\\hat{w}_t$ weights, we can proceed to running the Diff-in-Diff part of the Synthetic DiD estimator. For this part, it is better if we work with the data in the format of a table with $N$ by $T$ rows, where we have columns for the states, the years, the outcome, the post-treatment indicator and the treated unit indicator. To that table, we will add the time and unit weights. Since the time weight is in a series with a time index and the unit weights is in another series with unit index, we can simply join everything together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cdc78964",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:20.321274Z",
     "start_time": "2023-07-27T13:05:20.317456Z"
    }
   },
   "outputs": [],
   "source": [
    "def join_weights(data, unit_w, time_w, year_col, state_col, treat_col, post_col):\n",
    "    return (\n",
    "        data\n",
    "        .set_index([year_col, state_col])\n",
    "        .join(time_w)\n",
    "        .join(unit_w)\n",
    "        .reset_index()\n",
    "        .fillna({time_w.name: 1 / len(pd.unique(data.query(f\"{post_col}\")[year_col])),\n",
    "                 unit_w.name: 1 / len(pd.unique(data.query(f\"{treat_col}\")[state_col]))})\n",
    "        .assign(**{\"weights\": lambda d: (d[time_w.name] * d[unit_w.name]).round(10)})\n",
    "        .astype({treat_col: int, post_col: int}))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd589e60",
   "metadata": {},
   "source": [
    "This joining process will leave `null` for the unit weights in the treated group and for the time weights in the post-treatment period. Fortunately, because we use uniform weighting in both cases, it is pretty easy to fill out those `null`s. For the time weights, we fill with the average of the post-treatment dummy, which will be $1/T_{post}$; for the unit weights, we fill with the average of the treated dummy, which will be $1/N_{tr}$. Finally, we multiply both weights together.\n",
    " \n",
    "Here is the result we get by running this code on the Proposition 99 data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "26c0441f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:20.336754Z",
     "start_time": "2023-07-27T13:05:20.322788Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>cigsale</th>\n",
       "      <th>treated</th>\n",
       "      <th>after_treatment</th>\n",
       "      <th>time_weights</th>\n",
       "      <th>unit_weights</th>\n",
       "      <th>weights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1970</td>\n",
       "      <td>1</td>\n",
       "      <td>89.800003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-4.600034e-14</td>\n",
       "      <td>-1.360835e-16</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1971</td>\n",
       "      <td>1</td>\n",
       "      <td>95.400002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-4.582315e-14</td>\n",
       "      <td>-1.360835e-16</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1972</td>\n",
       "      <td>1</td>\n",
       "      <td>101.099998</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-5.274190e-14</td>\n",
       "      <td>-1.360835e-16</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1973</td>\n",
       "      <td>1</td>\n",
       "      <td>102.900002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-5.766356e-14</td>\n",
       "      <td>-1.360835e-16</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1974</td>\n",
       "      <td>1</td>\n",
       "      <td>108.199997</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-5.617979e-14</td>\n",
       "      <td>-1.360835e-16</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year state     cigsale  treated  after_treatment  time_weights  \\\n",
       "0  1970     1   89.800003        0                0 -4.600034e-14   \n",
       "1  1971     1   95.400002        0                0 -4.582315e-14   \n",
       "2  1972     1  101.099998        0                0 -5.274190e-14   \n",
       "3  1973     1  102.900002        0                0 -5.766356e-14   \n",
       "4  1974     1  108.199997        0                0 -5.617979e-14   \n",
       "\n",
       "   unit_weights  weights  \n",
       "0 -1.360835e-16      0.0  \n",
       "1 -1.360835e-16      0.0  \n",
       "2 -1.360835e-16      0.0  \n",
       "3 -1.360835e-16      0.0  \n",
       "4 -1.360835e-16      0.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "did_data = join_weights(data, unit_weights, time_weights,\n",
    "                        year_col=\"year\",\n",
    "                        state_col=\"state\",\n",
    "                        treat_col=\"treated\",\n",
    "                        post_col=\"after_treatment\")\n",
    "\n",
    "did_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7bd8fd1d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:20.341947Z",
     "start_time": "2023-07-27T13:05:20.338557Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3870967741935484"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"after_treatment\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c53d5534",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:20.348195Z",
     "start_time": "2023-07-27T13:05:20.343479Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08333333333333333"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/len(data.query(\"after_treatment==1\")[\"year\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53cf5894",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3caa8e38",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "167d3f1b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0d7dc7f5",
   "metadata": {},
   "source": [
    "Finally, all we have to do is estimate a Diff-in-Diff model with the weights we've just defined. The parameter estimate associated with the interaction term for the post-treatment period and treated dummy will be the Synthetic Difference-in-Differences estimate for the $ATT$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e010d396",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:20.360601Z",
     "start_time": "2023-07-27T13:05:20.349764Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>                <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>               <td>  120.4060</td> <td>    1.272</td> <td>   94.665</td> <td> 0.000</td> <td>  117.911</td> <td>  122.901</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>after_treatment</th>         <td>  -19.1905</td> <td>    1.799</td> <td>  -10.669</td> <td> 0.000</td> <td>  -22.720</td> <td>  -15.661</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>treated</th>                 <td>  -25.2601</td> <td>    1.799</td> <td>  -14.043</td> <td> 0.000</td> <td>  -28.789</td> <td>  -21.731</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>after_treatment:treated</th> <td>  -15.6054</td> <td>    2.544</td> <td>   -6.135</td> <td> 0.000</td> <td>  -20.596</td> <td>  -10.615</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "did_model = smf.wls(\"cigsale ~ after_treatment*treated\",\n",
    "                    data=did_data,\n",
    "                    weights=did_data[\"weights\"]+1e-10).fit()\n",
    "\n",
    "did_model.summary().tables[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bd680e1",
   "metadata": {},
   "source": [
    "This estimate is much smaller than the one we get with Diff-in-Diff, but that is not surprising. As we've already discussed, the Diff-in-Diff estimator is probably biased in this case, since we have pretty good reasons to question the parallel trends assumption. What is perhaps less obvious is why the SDID estimate is smaller than the traditional SC estimate. If we go back and look at the SC plot, we can see that cigarette sales in California started to fall below its synthetic control prior to Proposition 99. This is probably due to the fact that traditional Synthetic Control has to match treated and control units in the entire pre-treatment period, causing it to miss one year or the other. This is less of an issue in SDID, since the time weights allow us to focus just on the periods that are more similar to the post-intervention period. In this case, those were precisely the three years anteceding Proposition 99. \n",
    " \n",
    "To grasp what SDID is doing, we can plot the Diff-in-Diff lines for the treated (California) and the SDID Synthetic Control. Notice how we are projecting the trend we see in the synthetic control onto the treated unit to get the counterfactual $Y(0)_{tr, post}$. The difference between the dashed purple line and the lower solid purple line is the $ATT$. We start those lines in 1987 to show how the time weights zero out all periods but 1986, 87 and 88. The time weights are also shown in the small plot down below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4969890a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:20.547274Z",
     "start_time": "2023-07-27T13:05:20.361949Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_pre_period = (time_weights * time_weights.index).sum()\n",
    "avg_post_period = 1989 + (2000 - 1989) / 2\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(15,8), sharex=True, gridspec_kw={'height_ratios': [3, 1]})\n",
    "\n",
    "ax1.plot(data_piv.index, sc_did, label=\"California\")\n",
    "ax1.plot(data_piv.index, data_piv[\"california\"], label=\"Synthetic Control (SDID)\")\n",
    "ax1.vlines(1989, data_piv[\"california\"].min(), sc_did.max(), color=\"black\", ls=\"dotted\", label=\"Prop. 99\")\n",
    "\n",
    "pre_sc = did_model.params[\"Intercept\"]\n",
    "post_sc = pre_sc + did_model.params[\"after_treatment\"]\n",
    "pre_treat = pre_sc + did_model.params[\"treated\"]\n",
    "post_treat = post_sc + did_model.params[\"treated\"] + did_model.params[\"after_treatment:treated\"]\n",
    "\n",
    "sc_did_y0 = pre_treat + (post_sc - pre_sc)\n",
    "\n",
    "ax1.plot([avg_pre_period, avg_post_period], [pre_sc, post_sc], color=\"C2\")\n",
    "ax1.plot([avg_pre_period, avg_post_period], [pre_treat, post_treat], color=\"C2\", ls=\"dashed\")\n",
    "ax1.plot([avg_pre_period, avg_post_period], [pre_treat, sc_did_y0], color=\"C2\")\n",
    "\n",
    "ax1.annotate('ATT', xy=(1995, 69), xytext=(1996, 66.5), \n",
    "            fontsize=12, ha='center', va='bottom',\n",
    "            bbox=dict(boxstyle='square', fc='white', color='k'),\n",
    "            arrowprops=dict(arrowstyle='-[, widthB=1.5, lengthB=0.5', lw=2.0, color='k'))\n",
    "\n",
    "ax1.legend()\n",
    "ax1.set_title(\"Synthetic Diff-in-Diff\")\n",
    "ax1.set_ylabel(\"Cigarette Sales\")\n",
    "\n",
    "ax2.bar(time_weights.index, time_weights)\n",
    "ax2.vlines(1989, 0, 1, color=\"black\", ls=\"dotted\")\n",
    "ax2.set_ylabel(\"Time Weights\")\n",
    "ax2.set_xlabel(\"Years\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6140f080",
   "metadata": {},
   "source": [
    "The above estimator estimates the $ATT$ which is the effect of Propostion 99 on California averaged out across all post-treatment time periods. But, from the above plot, it looks like the effect increases over time. What if we want to take that into account? Fortunately, it is very straightforward to do that.\n",
    " \n",
    "Before we move on, just a word of caution about the above estimator. You should **not** trust the standard errors or confidence intervals reported by the regression we just ran. They don't reflect the variance in estimating the weights. We will take a look at how to do proper inference briefly, but first, let's see how to deal with effect heterogeneity across time.\n",
    " \n",
    "## Time Effect Heterogeneity and Staggered Adoption\n",
    " \n",
    "Fortunately, it is incredibly easy to estimate one effect for each time period using SDID. All we have to do is run it multiple times, one for each time period. To be more precise, let's say we have the following treatment assignment matrix, with just 4 time periods and 3 units. The last unit is the treated one.\n",
    " \n",
    "$$\n",
    "D = \\begin{bmatrix}\n",
    "    0_1 & 0_1 & 0_1 \\\\\n",
    "    0_2 & 0_2 & 0_2 \\\\\n",
    "    0_3 & 0_3 & 1_3 \\\\\n",
    "    0_4 & 0_4 & 1_4 \\\\\n",
    "\\end{bmatrix}\n",
    "$$\n",
    " \n",
    "Running SDID with the above matrix would give us the average $ATT$ across periods 3 and 4. What we can do to estimate the effect on each period individually is simply to partition the problem into two, one for each post-treatment time period. Then, we run SDID on the data where we only keep post-treatment period 3 and again on the data where we only keep post-treatment period 4. That is, we run SDID on each of the following matrices individually.\n",
    " \n",
    "$$\n",
    "D_1 = \\begin{bmatrix}\n",
    "    0_1 & 0_1 & 0_1 \\\\\n",
    "    0_2 & 0_2 & 0_2 \\\\\n",
    "    0_3 & 0_3 & 1_3 \\\\\n",
    "\\end{bmatrix}\n",
    "$$\n",
    " \n",
    " \n",
    "$$\n",
    "D_2 = \\begin{bmatrix}\n",
    "    0_1 & 0_1 & 0_1 \\\\\n",
    "    0_2 & 0_2 & 0_2 \\\\\n",
    "    0_4 & 0_4 & 1_4 \\\\\n",
    "\\end{bmatrix}\n",
    "$$\n",
    " \n",
    "To do that, it would be best if we first merge all the steps of SDID into a single function. That is, estimating the unit and time weights and running DiD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5881bb9e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:20.593893Z",
     "start_time": "2023-07-27T13:05:20.548962Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-15.605397234587002"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def synthetic_diff_in_diff(data, outcome_col, year_col, state_col, treat_col, post_col):\n",
    "    \n",
    "    # find the unit weights\n",
    "    unit_weights = fit_unit_weights(data,\n",
    "                                    outcome_col=outcome_col,\n",
    "                                    year_col=year_col,\n",
    "                                    state_col=state_col,\n",
    "                                    treat_col=treat_col,\n",
    "                                    post_col=post_col)\n",
    "    \n",
    "    # find the time weights\n",
    "    time_weights = fit_time_weights(data,\n",
    "                                    outcome_col=outcome_col,\n",
    "                                    year_col=year_col,\n",
    "                                    state_col=state_col,\n",
    "                                    treat_col=treat_col,\n",
    "                                    post_col=post_col)\n",
    "\n",
    "    # join weights into DiD Data\n",
    "    did_data = join_weights(data, unit_weights, time_weights,\n",
    "                            year_col=year_col,\n",
    "                            state_col=state_col,\n",
    "                            treat_col=treat_col,\n",
    "                            post_col=post_col)\n",
    "    \n",
    "    # run DiD\n",
    "    formula = f\"{outcome_col} ~ {post_col}*{treat_col}\"\n",
    "    did_model = smf.wls(formula, data=did_data, weights=did_data[\"weights\"]+1e-10).fit()\n",
    "    \n",
    "    return did_model.params[f\"{post_col}:{treat_col}\"]\n",
    "\n",
    "\n",
    "synthetic_diff_in_diff(data, \n",
    "                       outcome_col=\"cigsale\",\n",
    "                       year_col=\"year\",\n",
    "                       state_col=\"state\",\n",
    "                       treat_col=\"treated\",\n",
    "                       post_col=\"after_treatment\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c15a5bfc",
   "metadata": {},
   "source": [
    "Now that we have a way of easily running SDID, we can run it multiple times, filtering out all the post-treatment periods except the one for which we want the effect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "feccbe5a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:21.037766Z",
     "start_time": "2023-07-27T13:05:20.595663Z"
    }
   },
   "outputs": [],
   "source": [
    "effects = {year: synthetic_diff_in_diff(data.query(f\"~after_treatment|(year=={year})\"), \n",
    "                                        outcome_col=\"cigsale\",\n",
    "                                        year_col=\"year\",\n",
    "                                        state_col=\"state\",\n",
    "                                        treat_col=\"treated\",\n",
    "                                        post_col=\"after_treatment\")\n",
    "           for year in range(1989, 2001)}\n",
    "\n",
    "effects = pd.Series(effects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c6b33f96",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:21.111687Z",
     "start_time": "2023-07-27T13:05:21.045305Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(effects);\n",
    "plt.ylabel(\"Effect in Cigarette Sales\")\n",
    "plt.title(\"SDID Effect Estimate by Year\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff195a1a",
   "metadata": {},
   "source": [
    "As expected, the effect gets bigger as time passes. It starts small, but it gradually increases to what seems like a decrease in consumption of 25 cigarette packs per capita in 2020. \n",
    " \n",
    "Conveniently, running multiple SDID will also be important to deal with the staggered adoption case. With staggered addoption design, we have multiple treated units, which get the treatment at different time periods. For example, going back to our very simple assignment matrix, with 3 units and 4 time periods, let's say unit 1 never gets the treatment, unit 2 gets the treatment at period 4 and unit 3 gets the treatment at period 3. This would result in the following matrix\n",
    " \n",
    "$$\n",
    "D = \\begin{bmatrix}\n",
    "    0_1 & 0_1 & 0_1 \\\\\n",
    "    0_2 & 0_2 & 0_2 \\\\\n",
    "    0_3 & 0_3 & 1_3 \\\\\n",
    "    0_4 & 1_4 & 1_4 \\\\\n",
    "\\end{bmatrix}\n",
    "$$\n",
    " \n",
    "Notice that SDID can't handle that matrix, because we don't have a clear definition about what is a pre-treatment period (before time 4, in the case of the second unit or before period 3, in the case of the second unit) or what is a control unit (unit 2 could be a control for the treatment starting at period 3). The key in solving this is realizing we can delete columns (units) or rows (time periods) in that matrix in order to go back to the block assignment design.\n",
    " \n",
    "For example, we can create two block matrices from the one above by first deleting the 3rd time period and then another one where we delete the 4th time period.\n",
    " \n",
    "$$\n",
    "D_1 = \\begin{bmatrix}\n",
    "    0_1 & 0_1 & 0_1 \\\\\n",
    "    0_2 & 0_2 & 0_2 \\\\\n",
    "    0_4 & 1_4 & 1_4 \\\\\n",
    "\\end{bmatrix}\n",
    "$$\n",
    " \n",
    "$$\n",
    "D_2 = \\begin{bmatrix}\n",
    "    0_1 & 0_1 & 0_1 \\\\\n",
    "    0_2 & 0_2 & 0_2 \\\\\n",
    "    0_3 & 0_3 & 1_3 \\\\\n",
    "\\end{bmatrix}\n",
    "$$\n",
    " \n",
    " \n",
    "The result is two block matrices, which means we can run SDID in both of them. The result will be two $ATT$ estimates, which we can then combine with a weighted average, where the weights are the proportion of treated time and periods in each block. In our example, the weight for $ATT_1$ would be $2/3$ and the weight for $ATT_2$ would be 1/3.\n",
    " \n",
    "Alternatively, we could also have 2 block designs by removing columns, which would result in the following matrices\n",
    " \n",
    "$$\n",
    "D_1 = \\begin{bmatrix}\n",
    "    0_1 & 0_1  \\\\\n",
    "    0_2 & 0_2  \\\\\n",
    "    0_3 & 0_3  \\\\\n",
    "    0_4 & 1_4  \\\\\n",
    "\\end{bmatrix}\n",
    "$$\n",
    " \n",
    "$$\n",
    "D_2 = \\begin{bmatrix}\n",
    "    0_1 & 0_1 \\\\\n",
    "    0_2 & 0_2 \\\\\n",
    "    0_3 & 1_3 \\\\\n",
    "    0_4 & 1_4 \\\\\n",
    "\\end{bmatrix}\n",
    "$$\n",
    " \n",
    "where $D_1$ has units 1 and 2 and $D_2$ has units 1 and 4. \n",
    " \n",
    "Since we already saw how to estimate SDID for different time periods, let's look at this approach where we filter out units. Since we don't originally have a staggered adoption design in our Proposition 99 data, let's instead simulate one. We'll create 3 new states from our data and pretend they pass a law similar to Proposition 99, but in the year 1993. Maybe they were impressed with the results in California and wanted to try in their states too. Once they do, this law that they pass decreases cigarette consumption by 3% each year. We can visualize the average cigarette consumption for those  new states to better understand what is going on. In dashed black, we have the year in which these states pass this anti-tobacco law. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6a9082c0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:21.121639Z",
     "start_time": "2023-07-27T13:05:21.113075Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "np.random.seed(1)\n",
    "n = 3\n",
    "tr_state = (data\n",
    "            .query(f\"state.isin({list(np.random.choice(data['state'].unique(), n))})\")\n",
    "            .assign(**{\n",
    "                \"treated\": True,\n",
    "                \"state\": lambda d: \"new_\" + d[\"state\"].astype(str),\n",
    "                \"after_treatment\": lambda d: d[\"year\"] > 1992\n",
    "            })\n",
    "            # effect of 3% / year\n",
    "            .assign(**{\"cigsale\": lambda d: np.random.normal(d[\"cigsale\"] - \n",
    "                                                             d[\"cigsale\"]*(0.03*(d[\"year\"] - 1992))*d[\"after_treatment\"], 1)}))\n",
    "\n",
    "new_data = pd.concat([data, tr_state]).assign(**{\"after_treatment\": lambda d: np.where(d[\"treated\"], d[\"after_treatment\"], False)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "827ded51",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:21.285798Z",
     "start_time": "2023-07-27T13:05:21.122933Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_data_piv = new_data.pivot(\"year\", \"state\", \"cigsale\")\n",
    "\n",
    "new_tr_states = list(filter(lambda c: str(c).startswith(\"new\"), new_data_piv.columns))\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(new_data_piv.drop(columns=[\"california\"]+new_tr_states), color=\"C1\", alpha=0.3)\n",
    "plt.plot(new_data_piv.drop(columns=[\"california\"]+new_tr_states).mean(axis=1), lw=3, color=\"C1\", ls=\"dashed\", label=\"Control Avg.\")\n",
    "\n",
    "plt.plot(new_data_piv[\"california\"], color=\"C0\", label=\"California\")\n",
    "plt.plot(new_data_piv[new_tr_states].mean(axis=1), color=\"C4\", label=\"New Tr State\")\n",
    "\n",
    "plt.vlines(x=1988, ymin=40, ymax=300, linestyle=\":\", lw=2, label=\"Proposition 99\", color=\"black\")\n",
    "plt.vlines(x=1992, ymin=40, ymax=300, linestyle=\"dashed\", lw=2, label=\"New State Tr.\", color=\"black\")\n",
    "plt.legend()\n",
    "plt.ylabel(\"Cigarette Sales\")\n",
    "plt.title(\"Two Treatment Groups\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9923321e",
   "metadata": {},
   "source": [
    "We finally have this staggered adoption data. Now, we need to figure out how to filter out some states so we can break the problem into multiple block assignment cases. First, we can group states by when they passed the law. The following code does exactly that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "61050b09",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:21.294955Z",
     "start_time": "2023-07-27T13:05:21.287574Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1989: ['california'], 1993: ['new_13', 'new_38', 'new_9']}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assignment_blocks = (new_data.query(\"treated & after_treatment\")\n",
    "                     .groupby(\"state\")[\"year\"].min()\n",
    "                     .reset_index()\n",
    "                     .groupby(\"year\")[\"state\"].apply(list).to_dict())\n",
    "\n",
    "assignment_blocks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "089c7d1f",
   "metadata": {},
   "source": [
    "As you can see, we have two groups of states. One with only California, which was treated starting in 1989, and another with the three new states we've created, which were all treated starting in 1993. Now, we need to run SDID for each of those groups. We can easily do that, but keeping just the control units plus one of those groups. There is a catch, though. The `after_treatment` column will have a different meaning, depending on which group we are looking at. If we are looking at the group containing only California, `after_treatment` should be `year >= 1989`; if we are looking at the group with the new states, it should be `year >= 1993`. Fortunately, this is pretty easy to account for. All we need is to recreate the `after_treatment` in each iteration. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c0ad1cab",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:21.431072Z",
     "start_time": "2023-07-27T13:05:21.296287Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1989: -15.605397234587002, 1993: -17.249435402003648}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "staggered_effects = {year: synthetic_diff_in_diff(new_data\n",
    "                                                   .query(f\"~treated|(state.isin({states}))\")\n",
    "                                                   .assign(**{\"after_treatment\": lambda d: d[\"year\"] >= year}),\n",
    "                                                  outcome_col=\"cigsale\",\n",
    "                                                  year_col=\"year\",\n",
    "                                                  state_col=\"state\",\n",
    "                                                  treat_col=\"treated\",\n",
    "                                                  post_col=\"after_treatment\")\n",
    "                     for year, states in assignment_blocks.items()}\n",
    "\n",
    "staggered_effects"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4f98fd5",
   "metadata": {},
   "source": [
    "Not surprisingly, the $ATT$ estimate for the first group, the one with only California, is exactly the same as the one we've seen before. The other $ATT$ refers to the one we get with the new group of states. We have to combine them into a single $ATT$. This can be done with the weighted average we've explained earlier. \n",
    " \n",
    "First, we calculate the number of treated entries (`after_treatment & treated`) in each block. Then, we combine the $ATT$s using those weights. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "00821c55",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:21.436860Z",
     "start_time": "2023-07-27T13:05:21.432299Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weights:  {1989: 12, 1993: 24}\n",
      "ATT:  -16.701422679531433\n"
     ]
    }
   ],
   "source": [
    "weights = {year: sum((new_data[\"year\"] >= year) & (new_data[\"state\"].isin(states)))\n",
    "           for year, states in assignment_blocks.items()}\n",
    "\n",
    "att = sum([effect*weights[year]/sum(weights.values()) for year, effect in staggered_effects.items()])\n",
    "\n",
    "print(\"weights: \", weights)\n",
    "print(\"ATT: \", att)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6744cd91",
   "metadata": {},
   "source": [
    "Here, we have a total of 36 treatment instances: the usual 12 post-treatment periods for California plus 8 treatment periods (1993-2000) for each of the three new treatment states we've introduced. With that in mind, the weight for the first $ATT$ is $12/36$ and for the second $ATT$, $24/36$, which combines to the result above.\n",
    " \n",
    "## Placebo Variance Estimation\n",
    " \n",
    "This chapter is getting a bit too long, but there is one promise we haven't fulfilled yet. Remember how we said, in the very beginning, that SDID has better precision (lower error bars) when compared to Synthetic Controls? The reason is that the time and unit fixed effects in SDID capture a ton of the variation in the outcome, which in turn, reduces the variance of the estimator.\n",
    " \n",
    "Of course I wouldn't ask you to take my word for it, so next, we'll show how to place a confidence interval around the SDID estimate. It turns out there are many solutions to this problem, but only one that fits the case for a single treated unit, which is the case we have here since only California was treated. The idea is to run a series of placebo tests, where we pretend a unit from the control pool is treated, when it actually isn't. Then, we use SDID to estimate the $ATT$ of this placebo test and store its result. We re-run this step multiple times, sampling a control unit each time. In the end, we will have an array of placebo $ATT$s. The variance of this array is the placebo variance of the SDID effect estimate, which we can use to construct a confidence interval.\n",
    " \n",
    "$$\n",
    "\\hat{V}^{placebo}_{\\tau} = B^{-1}\\sum_{b=1}^B\\bigg(\\hat{\\tau}^{(b)} - \\bar{\\hat{\\tau}}^{(b)}\\bigg)^2\n",
    "$$\n",
    " \n",
    "$$\n",
    "\\tau \\in \\hat{\\tau}^{sdid} \\pm \\mathcal{z}_{\\alpha/2} \\sqrt{\\hat{V}_{\\tau}}\n",
    "$$\n",
    " \n",
    "In order to implement this, the first thing we need is a function which creates the placebo. This function will filter out the treated units, sample a single control unit and flip the `treated` column for that control unit from 0 to a 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d0812ac0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:21.440418Z",
     "start_time": "2023-07-27T13:05:21.438130Z"
    }
   },
   "outputs": [],
   "source": [
    "def make_random_placebo(data, state_col, treat_col):\n",
    "    control = data.query(f\"~{treat_col}\")\n",
    "    states = control[state_col].unique()\n",
    "    placebo_state = np.random.choice(states)\n",
    "    return control.assign(**{treat_col: control[state_col] == placebo_state})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cc445100",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:21.450315Z",
     "start_time": "2023-07-27T13:05:21.441554Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>cigsale</th>\n",
       "      <th>treated</th>\n",
       "      <th>after_treatment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1204</th>\n",
       "      <td>39</td>\n",
       "      <td>1996</td>\n",
       "      <td>110.300003</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1205</th>\n",
       "      <td>39</td>\n",
       "      <td>1997</td>\n",
       "      <td>108.800003</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1206</th>\n",
       "      <td>39</td>\n",
       "      <td>1998</td>\n",
       "      <td>102.900002</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1207</th>\n",
       "      <td>39</td>\n",
       "      <td>1999</td>\n",
       "      <td>104.800003</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1208</th>\n",
       "      <td>39</td>\n",
       "      <td>2000</td>\n",
       "      <td>90.500000</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     state  year     cigsale  treated  after_treatment\n",
       "1204    39  1996  110.300003     True             True\n",
       "1205    39  1997  108.800003     True             True\n",
       "1206    39  1998  102.900002     True             True\n",
       "1207    39  1999  104.800003     True             True\n",
       "1208    39  2000   90.500000     True             True"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "placebo_data = make_random_placebo(data, state_col=\"state\", treat_col=\"treated\")\n",
    "\n",
    "placebo_data.query(\"treated\").tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ede6a51",
   "metadata": {},
   "source": [
    "In the example above, we've sampled state 39 and we are now pretending that it was treated. Notice how the `treated` column was flipped to `True`.\n",
    " \n",
    "The next thing we need is to compute the SDID estimate with this placebo data and repeat that a bunch of times. The next function does that. It runs the `synthetic_diff_in_diff` function to get the SDID estimate, but instead of passing the usual data, we pass the result of calling `make_random_placebo`. We do that multiple times to get an array of SDID estimates and, finally, compute the square root of the variance of this array, which is just the standard deviation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "de947896",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:21.455825Z",
     "start_time": "2023-07-27T13:05:21.451955Z"
    }
   },
   "outputs": [],
   "source": [
    "from joblib import Parallel, delayed # for parallel processing\n",
    "\n",
    "\n",
    "def estimate_se(data, outcome_col, year_col, state_col, treat_col, post_col, bootstrap_rounds=400, seed=0, njobs=4):\n",
    "    np.random.seed(seed=seed)\n",
    "    \n",
    "    sdid_fn = partial(synthetic_diff_in_diff,\n",
    "                      outcome_col=outcome_col,\n",
    "                      year_col=year_col,\n",
    "                      state_col=state_col,\n",
    "                      treat_col=treat_col,\n",
    "                      post_col=post_col)\n",
    "    \n",
    "    effects = Parallel(n_jobs=njobs)(delayed(sdid_fn)(make_random_placebo(data, state_col=state_col, treat_col=treat_col))\n",
    "                                     for _ in range(bootstrap_rounds))\n",
    "    \n",
    "    return np.std(effects, axis=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "34681a79",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:27.130575Z",
     "start_time": "2023-07-27T13:05:21.457046Z"
    }
   },
   "outputs": [],
   "source": [
    "effect = synthetic_diff_in_diff(data,\n",
    "                                outcome_col=\"cigsale\",\n",
    "                                year_col=\"year\",\n",
    "                                state_col=\"state\",\n",
    "                                treat_col=\"treated\",\n",
    "                                post_col=\"after_treatment\")\n",
    "\n",
    "\n",
    "se = estimate_se(data,\n",
    "                 outcome_col=\"cigsale\",\n",
    "                 year_col=\"year\",\n",
    "                 state_col=\"state\",\n",
    "                 treat_col=\"treated\",\n",
    "                 post_col=\"after_treatment\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9baae276",
   "metadata": {},
   "source": [
    "The standard deviation can then be used to construct confidence intervals much like we described in the formula above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "88f46e75",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:27.135013Z",
     "start_time": "2023-07-27T13:05:27.132200Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Effect: -15.605397234587002\n",
      "Standard Error: 9.912089736240311\n",
      "90% CI: (-31.960345299383516, 0.7495508302095111)\n"
     ]
    }
   ],
   "source": [
    "print(f\"Effect: {effect}\")\n",
    "print(f\"Standard Error: {se}\")\n",
    "print(f\"90% CI: ({effect-1.65*se}, {effect+1.65*se})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b5dae9a",
   "metadata": {},
   "source": [
    "Notice that the $ATT$ is not significant in this case, but what's more interesting here is to compare the standard error of the SDID estimate with the one we get from traditional Synthetic Control."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c11cb28d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:32.400320Z",
     "start_time": "2023-07-27T13:05:27.136625Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "def synthetic_control(data, outcome_col, year_col, state_col, treat_col, post_col):\n",
    "    \n",
    "    x_pre_control = (data\n",
    "                     .query(f\"~{treat_col}\")\n",
    "                     .query(f\"~{post_col}\")\n",
    "                     .pivot(year_col, state_col, outcome_col)\n",
    "                     .values)\n",
    "    \n",
    "    y_pre_treat_mean = (data\n",
    "                        .query(f\"~{post_col}\")\n",
    "                        .query(f\"{treat_col}\")\n",
    "                        .groupby(year_col)\n",
    "                        [outcome_col]\n",
    "                        .mean())\n",
    "    \n",
    "    w = cp.Variable(x_pre_control.shape[1])\n",
    "    objective = cp.Minimize(cp.sum_squares(x_pre_control@w - y_pre_treat_mean.values))\n",
    "    constraints = [cp.sum(w) == 1, w >= 0]\n",
    "    \n",
    "    problem = cp.Problem(objective, constraints)\n",
    "    problem.solve(verbose=False)\n",
    "    \n",
    "    sc = (data\n",
    "          .query(f\"~{treat_col}\")\n",
    "          .pivot(year_col, state_col, outcome_col)\n",
    "          .values) @ w.value\n",
    "    \n",
    "    y1 = data.query(f\"{treat_col}\").query(f\"{post_col}\")[outcome_col]\n",
    "    att = np.mean(y1 - sc[-len(y1):])\n",
    "    \n",
    "    return att\n",
    "\n",
    "\n",
    "def estimate_se_sc(data, outcome_col, year_col, state_col, treat_col, post_col, bootstrap_rounds=400, seed=0):\n",
    "    np.random.seed(seed=seed)\n",
    "    effects = [synthetic_control(make_random_placebo(data, state_col=state_col, treat_col=treat_col), \n",
    "                                 outcome_col=outcome_col,\n",
    "                                 year_col=year_col,\n",
    "                                 state_col=state_col,\n",
    "                                 treat_col=treat_col,\n",
    "                                 post_col=post_col)\n",
    "              for _ in range(bootstrap_rounds)]\n",
    "    \n",
    "    return np.std(effects, axis=0)\n",
    "\n",
    "\n",
    "effect_sc = synthetic_control(data,\n",
    "                              outcome_col=\"cigsale\",\n",
    "                              year_col=\"year\",\n",
    "                              state_col=\"state\",\n",
    "                              treat_col=\"treated\",\n",
    "                              post_col=\"after_treatment\")\n",
    "\n",
    "\n",
    "se_sc = estimate_se_sc(data,\n",
    "                       outcome_col=\"cigsale\",\n",
    "                       year_col=\"year\",\n",
    "                       state_col=\"state\",\n",
    "                       treat_col=\"treated\",\n",
    "                       post_col=\"after_treatment\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1976efca",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:05:32.404055Z",
     "start_time": "2023-07-27T13:05:32.401469Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Effect: -19.513629763998537\n",
      "Standard Error: 11.2419349486209\n",
      "90% CI: (-38.06282242922302, -0.9644370987740523)\n"
     ]
    }
   ],
   "source": [
    "print(f\"Effect: {effect_sc}\")\n",
    "print(f\"Standard Error: {se_sc}\")\n",
    "print(f\"90% CI: ({effect_sc-1.65*se_sc}, {effect_sc+1.65*se_sc})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb38187d",
   "metadata": {},
   "source": [
    "Notice how the error for synthetic control is higher than for SDID. Again, that is because SDID captures a lot of the variance in the outcome via its time and unit fixed effects. With this, we fulfill the promise we made earlier. But, before we close, it is worth mentioning that we can also use the same procedure to estimate the variance to make a confidence interval around the effect we've estimated for each post-treatment time period. All we need to do is run the code above once for each time period. Just keep in mind that this might take some time to run, even with the parallelization we've implemented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "33b20194",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:06:15.892324Z",
     "start_time": "2023-07-27T13:05:32.405591Z"
    }
   },
   "outputs": [],
   "source": [
    "standard_errors = {year: estimate_se(data.query(f\"~after_treatment|(year=={year})\"), \n",
    "                                     outcome_col=\"cigsale\",\n",
    "                                     year_col=\"year\",\n",
    "                                     state_col=\"state\",\n",
    "                                     treat_col=\"treated\",\n",
    "                                     post_col=\"after_treatment\")\n",
    "                   for year in range(1989, 2001)}\n",
    "\n",
    "standard_errors = pd.Series(standard_errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ef039953",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-27T13:06:15.988857Z",
     "start_time": "2023-07-27T13:06:15.894464Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "\n",
    "plt.plot(effects, color=\"C0\")\n",
    "plt.fill_between(effects.index, effects-1.65*standard_errors, effects+1.65*standard_errors, alpha=0.2,  color=\"C0\")\n",
    "\n",
    "plt.ylabel(\"Effect in Cigarette Sales\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.title(\"Synthetic DiD 90% Conf. Interval\")\n",
    "plt.xticks(rotation=45);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "404b9507",
   "metadata": {},
   "source": [
    "## Key Concepts\n",
    "\n",
    "Synthetic-Diff-in-Diff (SDID) draws inspiration from both Diff-in-Diff and Synthetic Control, which brings advantages from both models.  Like SC, SDID still works with multiple periods when pre-treatment trends are not parallel. However, unlike SC, SDID estimates unit weights to build a control unit which is only parallel to the treated group (it doesn't have to match its level). From DID, SDID leverages time and unit fixed effects, which helps to explain a lot of the variance in the outcome, which in turn reduces the variance of the SDID estimator. Synthetic-Diff-in-Diff also introduces some new ideas of its own. First, there is an additional $L2$ penalty in the optimization of the unit weights which makes them more spread out across control units. Second, SDID allows for an intercept (and hence, extrapolation) when building such weights. Third, SDID introduces the use of time weights, which are not present in either DID nor SC. For this reason, I wouldn't say SDID is just merging SC and SDID. It is rather building something new, inspired by these two approaches. I also wouldn't say that SDID is better or worse than traditional Synthetic Control. Each of them have different properties that might be appropriate or not, depending on the situation. For example, you might find yourself in a situation where allowing the extrapolations from SDID is dangerous. In which case, SC might be a good alternative.  \n",
    "\n",
    "\n",
    "## References \n",
    " \n",
    "This chapter is essentially an explainer to the *Synthetic Difference in Differences* (2019) article, by Dmitry Arkhangelsky, Susan Athey, David A. Hirshberg, Guido W. Imbens and Stefan Wager. Additionally, I would love to recognize Masa Asami for his python implementation of SDID, pysynthdid. His code helped me make sure I didn't have any bugs in mine, which was extremely helpful. \n",
    " \n",
    " \n",
    "## Contribute\n",
    " \n",
    "Causal Inference for the Brave and True is an open-source material on causal inference, the statistics of science. It uses only free software, based in Python. Its goal is to be accessible monetarily and intellectually.\n",
    "If you found this book valuable and you want to support it, please go to [Patreon](https://www.patreon.com/causal_inference_for_the_brave_and_true). If you are not ready to contribute financially, you can also help by fixing typos, suggesting edits or giving feedback on passages you didn't understand. Just go to the book's repository and [open an issue](https://github.com/matheusfacure/python-causality-handbook/issues). Finally, if you liked this content, please share it with others who might find it useful and give it a [star on GitHub](https://github.com/matheusfacure/python-causality-handbook/stargazers)."
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